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Volume 12
Issue 7
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Machines, Volume 12, Issue 7 (July 2024) – 69 articles

Cover Story (view full-size image): The most commonly used actuators in soft robotics are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous due to the associated strong non-linearities. Identifying analytical models is often complex, and finite element analyses are preferred for evaluating strain and tension states. Alternatively, artificial intelligence algorithms can be used to follow model-free approaches. In this paper, Response Surface Methodology is adopted to identify a predictive model of the bending angle of soft pneumatic joints. Finally, a bioinspired application of the identified model is proposed by designing the soft joints and realizing an actuator that mimics the movements of the scorpion's tail in the attack position. View this paper
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23 pages, 7868 KiB  
Article
An Advanced Diagnostic Approach for Broken Rotor Bar Detection and Classification in DTC Controlled Induction Motors by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT Methodology
by Muhammad Amir Khan, Bilal Asad, Toomas Vaimann and Ants Kallaste
Machines 2024, 12(7), 495; https://doi.org/10.3390/machines12070495 - 22 Jul 2024
Cited by 2 | Viewed by 1434
Abstract
This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing [...] Read more.
This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC’s closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions. Full article
(This article belongs to the Special Issue Machine Learning Based Predictive Maintenance and Condition Monitoring)
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<p>Broken rotor bar cross-sectional views of induction motor for (<b>a</b>) healthy, (<b>b</b>) 1 BRB, and (<b>c</b>) 3 BRB.</p> Full article ">Figure 2
<p>Flow chart of DSHAP-IFS for feature selection using GBDT.</p> Full article ">Figure 3
<p>Proposed DSHAP-IFS feature importance plot.</p> Full article ">Figure 4
<p>(<b>a</b>) Healthy BRB (<b>b</b>) one BRB(<b>c</b>) two BRB (<b>d</b>) three BRB.</p> Full article ">Figure 5
<p>Practical setup with loading motor (<b>right side</b>) and testing motor (<b>left side</b>).</p> Full article ">Figure 6
<p>Proposed fault diagnostic architecture for DTC-controlled induction machine for BRB detection and classification.</p> Full article ">Figure 7
<p>Frequency domain analysis of current signals for healthy, 1 BRB, 2 BRB, and 3 BRB at different full-load scenarios.</p> Full article ">Figure 8
<p>Frequency domain analysis of voltage signals for healthy, 1 BRB, 2 BRB, and 3 BRB at different full load scenarios.</p> Full article ">Figure 9
<p>Frequency domain analysis of torque signals for healthy, 1 BRB, 2 BRB, and 3 BRB at different full load scenarios.</p> Full article ">Figure 10
<p>Frequency domain analysis of speed signals for healthy, 1 BRB, 2 BRB, and 3 BRB at different full load scenarios.</p> Full article ">Figure 11
<p>Confusion matrix analysis for a healthy state, 1 BRB, 2 BRBs, and 3 BRBs at 100% loading conditions.</p> Full article ">Figure 11 Cont.
<p>Confusion matrix analysis for a healthy state, 1 BRB, 2 BRBs, and 3 BRBs at 100% loading conditions.</p> Full article ">Figure 12
<p>Receiver operating characteristic curves for a healthy state, 1 BRB, 2BR, and 3 BRB at 100% loading conditions.</p> Full article ">Figure 13
<p>Performance measures for broken rotor bars (BRBs) under various loading conditions.</p> Full article ">
38 pages, 23238 KiB  
Review
Friction Stir Channeling in Heat Sink Applications: Innovative Manufacturing Approaches and Performance Evaluation
by Sooraj Patel and Amit Arora
Machines 2024, 12(7), 494; https://doi.org/10.3390/machines12070494 - 22 Jul 2024
Cited by 1 | Viewed by 1292
Abstract
The fabrication of compact heat exchangers with precisely designed micro- and mini-channels is crucial for enhancing the efficiency of thermal management systems. Friction stir channeling (FSC) emerges as a cost-effective advanced manufacturing process to create complex integral channels, offering channel shape and size [...] Read more.
The fabrication of compact heat exchangers with precisely designed micro- and mini-channels is crucial for enhancing the efficiency of thermal management systems. Friction stir channeling (FSC) emerges as a cost-effective advanced manufacturing process to create complex integral channels, offering channel shape and size flexibility. This review article highlights the pivotal role of processing parameters in channel formation and maintaining their integrity, necessitating a comprehensive understanding of material flow dynamics. A rigorous assessment has been conducted on the channel under mechanical stresses, including tension, bending, and fatigue. The paper emphasizes the potential of FSC to revolutionize heat sink applications by exploring the fundamental concepts, governing parameters, ongoing enhancements in tool design, microstructural and mechanical properties, and heat transfer performance. Full article
(This article belongs to the Special Issue Advancements in Emerging Additive Manufacturing Techniques for Multifunctional Sustainable Technologies)
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Figure 1
<p>Schematic of FSC process: (<b>a</b>) FSC tool and workpiece before processing (<b>b</b>,<b>c</b>) an integral channel fabricated in the workpiece.</p> Full article ">Figure 2
<p>FSC regions (A and B) Channel nugget (C) Base material (D) Channel (E) Material deposited on the workpiece underneath the shoulder. Reprinted from Ref. [<a href="#B21-machines-12-00494" class="html-bibr">21</a>] with permission from Elsevier.</p> Full article ">Figure 3
<p>A schematic of the FSC tool having a flat tool shoulder and threaded pin.</p> Full article ">Figure 4
<p>FSC tool with a scroll on the plane shoulder. Reprinted from Ref. [<a href="#B30-machines-12-00494" class="html-bibr">30</a>] with permission from Elsevier.</p> Full article ">Figure 5
<p>A schematic representation of (<b>a</b>) extracted material deposition on the workpiece surface during the FSC with clearance, and (<b>b</b>) removal of extracted material as flashes during FSC without clearance.</p> Full article ">Figure 6
<p>FSC tool geometry (<b>a</b>) a scroll design covering half of the tool shoulder surface, (<b>b</b>,<b>c</b>) scroll designs encompassing the entire tool shoulder surface. Reprinted from Ref. [<a href="#B31-machines-12-00494" class="html-bibr">31</a>] with permission from Elsevier.</p> Full article ">Figure 7
<p>FSC tool featuring (<b>a</b>) an upward conical tool pin, and (<b>b</b>) a straight cylindrical tool pin. Reproduced from Ref. [<a href="#B34-machines-12-00494" class="html-bibr">34</a>] with permission from Springer Nature.</p> Full article ">Figure 8
<p>Implementation of the tilt angle using the Upward Conical Pin (UCP) tool and physical tilting of the Straight Cylindrical Pin (SCP) tool. Reproduced from Ref. [<a href="#B34-machines-12-00494" class="html-bibr">34</a>] with permission from Springer Nature.</p> Full article ">Figure 9
<p>(<b>a</b>) Schematic of FSC process with stationary shoulder (<b>b</b>) material extraction from the vents (<b>c</b>) channel formation in serpentine profile (<b>d</b>) extruded wire from the expelled material [<a href="#B36-machines-12-00494" class="html-bibr">36</a>]. Courtesy of TWI Ltd.</p> Full article ">Figure 10
<p>(<b>a</b>) A schematic of the Hybrid Friction Stir Channeling (HFSC) process (<b>b</b>) butt and lap joint arrangements for the FSC in multiple components [<a href="#B20-machines-12-00494" class="html-bibr">20</a>,<a href="#B37-machines-12-00494" class="html-bibr">37</a>].</p> Full article ">Figure 11
<p>Schematic representation of FSC regions: Nugget, TMAZ, and HAZ.</p> Full article ">Figure 12
<p>(<b>a</b>) Cross-section of friction stir channel (<b>b</b>) Microstructure in nugget (<b>c</b>) TMAZ at AS (<b>d</b>) TMAZ at RS. Reprinted from Ref. [<a href="#B55-machines-12-00494" class="html-bibr">55</a>] with permission from Elsevier.</p> Full article ">Figure 13
<p>(<b>a</b>) Cross-section of the channel featuring regions surrounding the channel; (<b>b</b>) Horizontal protrusions and vertical material deposition at RS. Reprinted from Ref. [<a href="#B54-machines-12-00494" class="html-bibr">54</a>] with permission from Elsevier.</p> Full article ">Figure 14
<p>Material flow using the broken tool pin technique: Region 1 indicates the presence of a cavity on the AS, attributed to inadequate material flow (Region 2) that failed to fill the void. w and v represent tool rotation speed and traverse speed, respectively. Reprinted from Ref. [<a href="#B69-machines-12-00494" class="html-bibr">69</a>] with kind permission of Trans Tech Publications.</p> Full article ">Figure 15
<p>FSC channels formed in serpentine profile using an unthreaded upper conical pin with (<b>a</b>) the AS at the inward curve and (<b>b</b>) the AS at the outward curve. Used with permission of SAGE Publications Ltd. Journals, from Ref. [<a href="#B73-machines-12-00494" class="html-bibr">73</a>]; permission conveyed through Copyright Clearance Center, Inc.</p> Full article ">Figure 16
<p>Schematic for the material flow with (<b>a</b>) the AS at the inward curve and (<b>b</b>) the AS at the outward curve. Used with permission of SAGE Publications Ltd. Journals, from Ref. [<a href="#B73-machines-12-00494" class="html-bibr">73</a>]; permission conveyed through Copyright Clearance Center, Inc.</p> Full article ">Figure 17
<p>Variation of the dynamic recrystallization zone concerning the pseudo heat index. Reproduced from Ref. [<a href="#B77-machines-12-00494" class="html-bibr">77</a>] with permission from Springer Nature.</p> Full article ">Figure 18
<p>Channel cross-section area with different sets of tool rotational and traverse speeds for the plunge depths of (<b>a</b>) 2.8 mm and (<b>b</b>) 3 mm. Reprinted from Ref. [<a href="#B21-machines-12-00494" class="html-bibr">21</a>] with permission from Elsevier.</p> Full article ">Figure 19
<p>FSC using the unthreaded tool with the tilt angle for the tool rotation speed and a tool traverse speed of (<b>a</b>) 630 rpm and 25 mm/min; (<b>b</b>) 1000 rpm and 12 mm/min; and (<b>c</b>) 1000 rpm and 25 mm/min, respectively. Reproduced from Ref. [<a href="#B35-machines-12-00494" class="html-bibr">35</a>] with permission from Springer Nature.</p> Full article ">Figure 20
<p>Longitudinal cross-section of (<b>a</b>) RS and (<b>b</b>) AS walls at a tool rotational speed of 800 rpm and a traverse speed of 150 mm/min. Used with permission of Trans Tech Publications Ltd., from Ref. [<a href="#B71-machines-12-00494" class="html-bibr">71</a>]; permission conveyed through Copyright Clearance Center, Inc.</p> Full article ">Figure 21
<p>Channel bottom, RS wall, channel ceiling, and AS wall at tool rotational and traverse speeds of (<b>a</b>,<b>e</b>) 600 rpm and 80 mm/min, (<b>b</b>,<b>f</b>) 600 rpm and 150 mm/min, (<b>c</b>,<b>g</b>) 800 rpm and 80 mm/min, and (<b>d</b>,<b>h</b>) 800 rpm and 150 mm/min. The scale corresponds to a length of 1 mm. Used with permission of Trans Tech Publications Ltd., from Ref. [<a href="#B71-machines-12-00494" class="html-bibr">71</a>]; permission conveyed through Copyright Clearance Center, Inc.</p> Full article ">Figure 22
<p>Cross-section of the channels fabricated in aluminum alloy AA5083 at different tool-shoulder workpiece clearances: (<b>a</b>) 0.10 mm, (<b>b</b>) 0.25 mm, (<b>c</b>) 0.55 mm, and (<b>d</b>) 0.70 mm. Inset digits show channel cross-sectional area in mm<sup>2</sup>. Reproduced from Ref. [<a href="#B81-machines-12-00494" class="html-bibr">81</a>] with permission from Springer Nature.</p> Full article ">Figure 23
<p>Average load during the FSC at lower heat input (ω = 600 rpm, ν = 150 mm/min) and higher heat input (ω = 1600 rpm, ν = 40 mm/min) conditions for the tool shoulder—workpiece clearance of 0.8 mm and 1.2 mm. Reprinted from Ref. [<a href="#B19-machines-12-00494" class="html-bibr">19</a>] with permission from Elsevier.</p> Full article ">Figure 24
<p>The macroscopic images of the channel cross-sections for FSC using an unthreaded tool with tilt angles of (<b>a</b>) 2°, (<b>b</b>) 2.5°, and (<b>c</b>) 3°; (<b>d</b>) The material flow within the nugget region is illustrated by blue, purple, and green arrows, each representing distinct characteristics of flow. Reproduced from Ref. [<a href="#B35-machines-12-00494" class="html-bibr">35</a>] with permission from Springer Nature.</p> Full article ">Figure 25
<p>Channel formed during the FSC: (<b>a</b>) thread angle of 60° and depth of cut 0.2 mm, (<b>b</b>) thread angle of 60° and depth of cut 0.5 mm, and (<b>c</b>) thread angle of 75° and depth of cut 0.8 mm. Inset digits show channel cross-sectional area in mm<sup>2</sup>. Reprinted from Ref. [<a href="#B82-machines-12-00494" class="html-bibr">82</a>] with permission from Elsevier.</p> Full article ">Figure 26
<p>Cross-section of channels fabricated at 1000 rpm, 31.5 mm/min, and 0.8 mm clearance using (<b>a</b>) a straight cylindrical pin with tilt angle and (<b>b</b>) a UCP tool. Used with permission of SAGE Publications Ltd. Journals, from Ref. [<a href="#B73-machines-12-00494" class="html-bibr">73</a>]; permission conveyed through Copyright Clearance Center, Inc.</p> Full article ">Figure 27
<p>Microhardness at the cross-section of FSC for the aluminum alloy (<b>a</b>) AA 7178-T6 (base material hardness 194 HV0.5). Used with permission of Trans Tech Publications Ltd., from Ref. [<a href="#B71-machines-12-00494" class="html-bibr">71</a>]; permission conveyed through Copyright Clearance Center, Inc. (<b>b</b>) AA 5083-H111 (base material hardness 92 HV1). Reprinted from Ref. [<a href="#B30-machines-12-00494" class="html-bibr">30</a>] with permission from Elsevier.</p> Full article ">Figure 28
<p>Microhardness variation across the nugget zone for different processing parameters: (<b>a</b>) heat-treatable AA 6061. Reprinted from Ref. [<a href="#B21-machines-12-00494" class="html-bibr">21</a>] with permission from Elsevier. (<b>b</b>) Strain-hardenable AA 5083-H111 at different processing parameters of tool-pin depth, tool rotation speed, and tool traverse speed, respectively. Reprinted from Ref. [<a href="#B55-machines-12-00494" class="html-bibr">55</a>] with permission from Elsevier.</p> Full article ">Figure 29
<p>Micro-hardness mapping in the vicinity of the friction stir channel for the strain-hardenable aluminum alloy AA 5083-H111. Reproduced from Ref. [<a href="#B77-machines-12-00494" class="html-bibr">77</a>] with permission from Springer Nature.</p> Full article ">Figure 30
<p>(<b>a</b>) Stress-strain curve for the FSC specimen, (<b>b</b>) mechanical properties of the FSC specimen compared with the base material. Reprinted from Ref. [<a href="#B70-machines-12-00494" class="html-bibr">70</a>] with permission from Elsevier.</p> Full article ">Figure 31
<p>Four-point bending strength at different processing parameters. Reprinted from Ref. [<a href="#B30-machines-12-00494" class="html-bibr">30</a>] with permission from Elsevier.</p> Full article ">Figure 32
<p>The fatigue life at room temperature, 120 °C, and 200 °C under different maximum stress conditions. Reprinted from Ref. [<a href="#B30-machines-12-00494" class="html-bibr">30</a>] with permission from Elsevier.</p> Full article ">Figure 33
<p>(<b>a</b>) Microstructural variation at the interface of nugget and TMAZ at AS; (<b>b</b>) schematic representation of channel roof (1) and channel bottom (2) crack paths. The red dot denotes the location of crack initiation on the AS. Reprinted from Ref. [<a href="#B24-machines-12-00494" class="html-bibr">24</a>] with permission from Elsevier.</p> Full article ">Figure 34
<p>Ratchet marks at the channel surface. Reprinted from Ref. [<a href="#B24-machines-12-00494" class="html-bibr">24</a>] with permission from Elsevier.</p> Full article ">Figure 35
<p>SEM fractography of the fracture surfaces at (<b>a</b>) room temperature and (<b>b</b>) 200 °C. Reprinted from Ref. [<a href="#B24-machines-12-00494" class="html-bibr">24</a>] with permission from Elsevier.</p> Full article ">Figure 36
<p>Uniaxial fatigue strengths of friction stir channels and base material. Reprinted from Ref. [<a href="#B70-machines-12-00494" class="html-bibr">70</a>] with permission from Elsevier.</p> Full article ">Figure 37
<p>Friction-stirred channels in helical trajectories on tubular workpieces [<a href="#B36-machines-12-00494" class="html-bibr">36</a>]. Courtesy of TWI Ltd.</p> Full article ">Figure 38
<p>Hybrid FSC applied in fabricating thermal management systems for EV battery cells cooling: (<b>a</b>) A schematic of hybrid FSC, (<b>b</b>) material stirring action due to welding and channeling probe feature, (<b>c</b>,<b>d</b>) hybrid FSC to fabricate channels in 8-mm thick aluminum alloy AA5083 and simultaneously weld it with 3-mm thick copper for the EV battery cells thermal management systems [<a href="#B37-machines-12-00494" class="html-bibr">37</a>].</p> Full article ">Figure 39
<p>(<b>a</b>) Experimental setup to measure axial wall conduction, (<b>b</b>) schematic of an experimental setup, (<b>c</b>) placements of thermocouples and pressure connectors. Reproduced from Ref. [<a href="#B99-machines-12-00494" class="html-bibr">99</a>] with permission from Springer Nature.</p> Full article ">Figure 40
<p>Boundary conditions for the heat transfer measurement using friction stir channeling. Reproduced from Ref. [<a href="#B99-machines-12-00494" class="html-bibr">99</a>] with permission from Springer Nature.</p> Full article ">Figure 41
<p>(<b>a</b>) Experimental results of local wall temperature and fluid flow temperature along the channel length, (<b>b</b>) validation of local wall temperature and fluid flow temperature (<b>c</b>) with the numerical prediction, (<b>d</b>) experimental and numerical results of wall and fluid flow temperature differences along the channel length, (<b>e</b>) numerical heat flux throughout the channel length. Reproduced from Ref. [<a href="#B99-machines-12-00494" class="html-bibr">99</a>] with permission from Springer Nature.</p> Full article ">Figure 42
<p>Cooling performance of channels fabricated in aluminum and copper: (<b>a</b>) Change in temperature of water over time when compressed air at different flow rates is used as a working fluid; (<b>b</b>) Cooling power of channels fabricated in aluminum and copper at different flow rates (lpm—liters per minute). Reprinted from Ref. [<a href="#B100-machines-12-00494" class="html-bibr">100</a>] with permission from Elsevier.</p> Full article ">Figure 43
<p>Experimental setup to measure the thermal performance: (<b>a</b>) top view of the channeling path and heat sources, (<b>b</b>) schematic front view, and (<b>c</b>) experimental setup [<a href="#B20-machines-12-00494" class="html-bibr">20</a>].</p> Full article ">Figure 44
<p>Thermal performance of HFSC and milled channels: (<b>a</b>) cooling of the heatsink concerning the Reynolds number, (<b>b</b>) cooling rate of the heatsink, (<b>c</b>) pump power to flow the coolant for different Reynolds numbers, (<b>d</b>) cooling efficiency at different power inputs [<a href="#B20-machines-12-00494" class="html-bibr">20</a>].</p> Full article ">
25 pages, 6740 KiB  
Article
Design of Active Posture Controller for Trailing-Arm Vehicle: Improving Path-Following and Handling Stability
by Zheng Pan, Boyuan Li, Shiyu Zhou, Shaoxun Liu, Shouyuan Chen and Rongrong Wang
Machines 2024, 12(7), 493; https://doi.org/10.3390/machines12070493 - 22 Jul 2024
Viewed by 888
Abstract
To address the question of which posture trailing-arm vehicles (TAVs) should be adopted while driving, this study introduces an innovative active posture controller (APC) to improve both path-following and handling stability performance. Leveraging a nonlinear tire model that considers corner load variation and [...] Read more.
To address the question of which posture trailing-arm vehicles (TAVs) should be adopted while driving, this study introduces an innovative active posture controller (APC) to improve both path-following and handling stability performance. Leveraging a nonlinear tire model that considers corner load variation and wheel camber, alongside the kinematics and double-track model of TAVs, the impact of vehicle body posture on handling performance has been investigated. To fully utilize the four-wheel independent drive and posture adjustable characteristics of the TAV mechanisms, an integrated nonlinear model predictive control (NMPC) combining APC and tire forces distribution is devised. Through simulations conducted using Simulink-Multibody (2023a), the effectiveness of the proposed controller is demonstrated, particularly when compared to the scheme that does not account for the unique posture adjustment mechanisms of TAVs. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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<p>Geometry-based steering policy of TAVs.</p> Full article ">Figure 2
<p>Planer path-following model.</p> Full article ">Figure 3
<p>Rolling and pitching geometry of the TAV. (<b>a</b>) Side view. (<b>b</b>) Rear view.</p> Full article ">Figure 4
<p>Double-track TAV model.</p> Full article ">Figure 5
<p>Block diagram of overall controller structure.</p> Full article ">Figure 6
<p>The workspace of the TAV posture. (<b>a</b>) Posture cone. (<b>b</b>) The PC constraint used in the NMPC controller.</p> Full article ">Figure 7
<p>A trailing-arm suspension platform in the Simscape Multibody environment.</p> Full article ">Figure 8
<p>Lateral and heading errors on lane-keeping condition.</p> Full article ">Figure 9
<p>Yaw rates and sideslips on lane-keeping condition.</p> Full article ">Figure 10
<p>Posture control on lane-keeping condition.</p> Full article ">Figure 11
<p>Yaw moments of CoM on lane-keeping condition.</p> Full article ">Figure 12
<p>Trajectories on S-turn with smaller path curvature case.</p> Full article ">Figure 13
<p>Lateral and heading errors on S-turn with smaller path curvature case.</p> Full article ">Figure 14
<p>Yaw rates and sideslips on S-turn with smaller path curvature case.</p> Full article ">Figure 15
<p>Posture control on S-turn with smaller path curvature case.</p> Full article ">Figure 16
<p>Yaw moments of CoM on S-turn condition with smaller path curvature case.</p> Full article ">Figure 17
<p>Trajectories on S-turn condition with larger path curvature case.</p> Full article ">Figure 18
<p>Lateral and heading errors on S-turn with larger path curvature case.</p> Full article ">Figure 19
<p>Yaw rates and sideslips on S-turn with larger path curvature case.</p> Full article ">Figure 20
<p>Posture control on S-turn with larger path curvature case.</p> Full article ">Figure 21
<p>Yaw moments of CoM on S-turn with larger path curvature case.</p> Full article ">
22 pages, 7557 KiB  
Article
Fault Diagnosis Method for Tractor Transmission System Based on Improved Convolutional Neural Network–Bidirectional Long Short-Term Memory
by Liyou Xu, Guoxiang Zhao, Sixia Zhao, Yiwei Wu and Xiaoliang Chen
Machines 2024, 12(7), 492; https://doi.org/10.3390/machines12070492 - 21 Jul 2024
Cited by 1 | Viewed by 1176
Abstract
In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an [...] Read more.
In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an enhanced convolutional neural network (CNN) and a bidirectional long short-term memory neural network (BILSTM). This approach uses a one-dimensional convolutional neural network (1DCNN) to create three feature extractors of varying scales, directly extracting feature information from different levels of the raw vibration signals. Simultaneously, in order to enhance the model’s predicted accuracy and learn the data features more effectively, it presents the multi-head attention mechanism (MHA). To overcome the issue of high noise levels in tractor working environments and enhance the model’s robustness, an adaptive soft threshold is introduced. Finally, to recognize and classify faults, the fused feature data are fed into a classifier made up of bidirectional long short-term memory (BILSTM) and fully linked layers. The analytical findings demonstrate that the fault recognition accuracy of the method described in this article is over 98%, and it also has better performance in noisy environments. Full article
(This article belongs to the Special Issue Advanced Signal Processing Methods and Deep Neural Networks for Machine Fault Diagnosis)
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Figure 1
<p>Convolutional neural network structure diagram.</p> Full article ">Figure 2
<p>Multi-head attention structure diagram.</p> Full article ">Figure 3
<p>BILSTM structure diagram.</p> Full article ">Figure 4
<p>Network structure diagram.</p> Full article ">Figure 5
<p>Fault diagnosis flowchart.</p> Full article ">Figure 6
<p>Original vibration signal plot.</p> Full article ">Figure 7
<p>Tractor transmission system loading test bench.</p> Full article ">Figure 8
<p>Schematic diagram of sampling point location.</p> Full article ">Figure 9
<p>Original vibration diagram of gears.</p> Full article ">Figure 10
<p>Loss function and accuracy curve plot. (<b>a</b>) The CWRU dataset’s loss variation; (<b>b</b>) the CWRU dataset’s accuracy; (<b>c</b>) the laboratory-collected dataset’s loss variation; (<b>d</b>) the laboratory-collected dataset’s accuracy.</p> Full article ">Figure 10 Cont.
<p>Loss function and accuracy curve plot. (<b>a</b>) The CWRU dataset’s loss variation; (<b>b</b>) the CWRU dataset’s accuracy; (<b>c</b>) the laboratory-collected dataset’s loss variation; (<b>d</b>) the laboratory-collected dataset’s accuracy.</p> Full article ">Figure 11
<p>Confusion matrix. (<b>a</b>) The CWRU dataset’s confusion matrix; (<b>b</b>) the laboratory-collected dataset’s confusion matrix.</p> Full article ">Figure 12
<p>t-SNE visualization. (<b>a</b>) CWRU dataset’s visualization; (<b>b</b>) laboratory-collected dataset’s visualization.</p> Full article ">Figure 13
<p>Confusion matrix comparison.</p> Full article ">Figure 14
<p>t-SNE visualization comparison.</p> Full article ">Figure 15
<p>Confusion matrix.</p> Full article ">Figure 16
<p>t-SNE visualization.</p> Full article ">Figure 17
<p>The accuracy variation in the CWRU bearing dataset in noisy environments.</p> Full article ">Figure 18
<p>The accuracy variation in the laboratory-collected gearbox data under noisy conditions.</p> Full article ">
18 pages, 10016 KiB  
Article
Prediction and Dynamic Simulation Verification of Output Characteristics of Radial Piston Motors Based on Neural Networks
by Chunjin Li, Zhengwen Xia and Yongjie Tang
Machines 2024, 12(7), 491; https://doi.org/10.3390/machines12070491 - 20 Jul 2024
Viewed by 878
Abstract
Radial piston motors are executive components in hydraulic systems, tasked with providing appropriate torque and speed according to load requirements in practical applications. The purpose of this study is to predict the output torque of radial piston hydraulic motors and confirm their suitable [...] Read more.
Radial piston motors are executive components in hydraulic systems, tasked with providing appropriate torque and speed according to load requirements in practical applications. The purpose of this study is to predict the output torque of radial piston hydraulic motors and confirm their suitable operating conditions. Efficiency determination experiments were conducted on physical models, yielding thirty sets of performance data. Torque (output torque) and mechanical efficiency from the experimental data were selected as prediction targets and fitted using two methods: multiple linear regression and neural networks. A dynamic simulation model was built using Adams2020 software to obtain theoretical torque values, enabling the verification of the alignment between the predicted values and simulation results. The results indicate that the error between the theoretical torque of the dynamic model and the physical experiments is 1.9%, with the error of the neural network predictions being within 2%. The dynamic simulation model can yield highly accurate theoretical torque values, providing a reference for the external load of hydraulic motors; additionally, neural networks offer accurate predictions of output torque, thus reducing experimental testing costs. Full article
(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis)
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<p>Radial Piston Motor Structure Diagram.</p> Full article ">Figure 2
<p>Contact Force between Roller and Stator.</p> Full article ">Figure 3
<p>Schematic diagram of hydraulic system: 1 Filter; 2 Hydraulic pump; 3 Motor; 4, 14 Relief valve; 5, 8, 13 Pressure gauge; 6 Throttle valve; 7, 15, 16 Flow meter; 9 Radial plunger motor; 10 Coupling; 11 Torque sensor; 12 Load motor.</p> Full article ">Figure 4
<p>Radial plunger motor test platform.</p> Full article ">Figure 5
<p>Scatterplot matrix.</p> Full article ">Figure 6
<p>This is a figure. Schemes follow the same formatting.</p> Full article ">Figure 7
<p>Neural Network Flowchart.</p> Full article ">Figure 8
<p>Neural Network Architecture.</p> Full article ">Figure 9
<p>The fitting status of the output torque neural network: (<b>a</b>) represents the fitting results for the 23 training set samples; (<b>b</b>) represents the fitting results for the 7 testing set samples.</p> Full article ">Figure 10
<p>Outlet pressure: 0.3 MPa. (<b>a</b>) Torque Variation Diagram; (<b>b</b>) Mechanical Efficiency Variation Diagram.</p> Full article ">Figure 11
<p>Outlet pressure: 0.5 MPa. (<b>a</b>) Torque Variation Diagram; (<b>b</b>) Mechanical Efficiency Variation Diagram.</p> Full article ">Figure 12
<p>Outlet pressure: 0.7 MPa. (<b>a</b>) Torque Variation Diagram; (<b>b</b>) Mechanical Efficiency Variation Diagram.</p> Full article ">Figure 13
<p>The structure of the dynamic model.</p> Full article ">Figure 14
<p>Three-Dimensional Models in Adams.</p> Full article ">Figure 15
<p>Material selection: steel.</p> Full article ">Figure 16
<p>Constraint conditions.</p> Full article ">Figure 17
<p>Drive and force in ADAMS.</p> Full article ">Figure 18
<p>Simulated Torque Result.</p> Full article ">
19 pages, 2956 KiB  
Article
Industrial Robot Trajectory Optimization Based on Improved Sparrow Search Algorithm
by Fei Ma, Weiwei Sun, Zhouxiang Jiang, Shuangfu Suo, Xiao Wang and Yue Liu
Machines 2024, 12(7), 490; https://doi.org/10.3390/machines12070490 - 20 Jul 2024
Cited by 2 | Viewed by 1217
Abstract
This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward [...] Read more.
This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot. Full article
(This article belongs to the Special Issue Industry 4.0: Intelligent Robots in Smart Manufacturing)
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<p>(<b>a</b>,<b>b</b>) Connecting rod dimensions of the robot; (<b>c</b>) the position of each joint axis of the robot.</p> Full article ">Figure 2
<p>The coordinate system of the robot.</p> Full article ">Figure 3
<p>The schematic of hybrid polynomial interpolation.</p> Full article ">Figure 4
<p>The flowchart of improved sparrow search algorithm.</p> Full article ">Figure 5
<p>The Cartesian spatial planning trajectories.</p> Full article ">Figure 6
<p>(<b>a</b>–<b>f</b>) fitness convergence curve of the robot joints 1–6.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>–<b>f</b>) fitness convergence curve of the robot joints 1–6.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>c</b>) The angular displacement curve obtained after using the ISSA, SSA, and PSO algorithms; (<b>d</b>–<b>f</b>) the angular velocity curve obtained after using the ISSA, SSA, and PSO algorithms; (<b>g</b>–<b>i</b>) the angular acceleration curve obtained after using the ISSA, SSA, and PSO algorithms.</p> Full article ">
18 pages, 17427 KiB  
Article
Damage Mechanism Analysis of the Connecting Screw of Turbine Disk-Drum Assembly
by Haijun Wang, Shengxu Wang, Pu Xue, Yongxin Guo and Liang Jiang
Machines 2024, 12(7), 489; https://doi.org/10.3390/machines12070489 - 19 Jul 2024
Viewed by 696
Abstract
The turbine disk-drum is one of the key components of an aero-engine and its assembly is connected with high-strength refined screws. But due to the uncoordinated rotation and deformation, the screws have abnormal wear damage. Through detailed contact stress analysis of screw body [...] Read more.
The turbine disk-drum is one of the key components of an aero-engine and its assembly is connected with high-strength refined screws. But due to the uncoordinated rotation and deformation, the screws have abnormal wear damage. Through detailed contact stress analysis of screw body and component level using the finite element method, combined with experimental observation, the mechanism of wear damage of screw surface in screws is determined. It mainly includes the following: Firstly, the finite element method is used to calculate the deformation and stress distribution of the connecting screw of the turbine disk-drum assembly. Then, after the overspeed test, the morphology of the screws disassembled from the disk-drum assembly is evaluated. It was found that the wear degree in the circumferential direction and axial direction of the screw was quite different, that is, the screw wear experiment was consistent with the finite element analysis results. Finally, the influence of different rotation states and screw tightening states on screw wear was compared and analyzed. Conclusions obtained in this paper will be helpful to improve the assembly reliability of turbine drum. Full article
(This article belongs to the Special Issue Advances in Intelligent Fault Diagnosis of Rotating Machinery)
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<p>Turbine disk-drum assembly.</p> Full article ">Figure 2
<p>Section view of turbine disk-drum assembly.</p> Full article ">Figure 3
<p>The force condition of disk-drum assembly.</p> Full article ">Figure 4
<p>Grid division results.</p> Full article ">Figure 5
<p>Contact settings.</p> Full article ">Figure 6
<p>Load and constraint setting.</p> Full article ">Figure 7
<p>Screw distribution.</p> Full article ">Figure 8
<p>Overall component deformation.</p> Full article ">Figure 9
<p>Deformation cloud picture.</p> Full article ">Figure 10
<p>Equivalent stress cloud picture.</p> Full article ">Figure 11
<p>Contact stress distribution.</p> Full article ">Figure 12
<p>Stress (<b>a</b>) and deformation (<b>b</b>) cloud picture of screw No.1.</p> Full article ">Figure 13
<p>Deformation cloud picture at different speeds. (<b>a</b>) 18,000 rpm; (<b>b</b>) 12,000 rpm; (<b>c</b>) 8000 rpm.</p> Full article ">Figure 14
<p>The equivalent stress cloud picture of the screw section at different rotational speeds. (<b>a</b>) 18,000 rpm; (<b>b</b>) 12,000 rpm; (<b>c</b>) 8000 rpm.</p> Full article ">Figure 15
<p>Nozzle deformation at different speeds. (<b>a</b>) 18,000 rpm; (<b>b</b>) 12,000 rpm; (<b>c</b>) 8000 rpm.</p> Full article ">Figure 16
<p>Preload distribution type. (<b>a</b>): Double-peak and double-high pre-tightening force distribution; (<b>b</b>): double-peak single-high preload distribution; (<b>c</b>): single-peak single-high pre-tightening force distribution; (<b>d</b>): uniform pre-tightening force distribution.</p> Full article ">Figure 16 Cont.
<p>Preload distribution type. (<b>a</b>): Double-peak and double-high pre-tightening force distribution; (<b>b</b>): double-peak single-high preload distribution; (<b>c</b>): single-peak single-high pre-tightening force distribution; (<b>d</b>): uniform pre-tightening force distribution.</p> Full article ">Figure 17
<p>Stress distribution.</p> Full article ">Figure 18
<p>Pre-tightening force and maximum stress.</p> Full article ">Figure 19
<p>Schematic picture of the overspeed test bench.</p> Full article ">Figure 20
<p>Test condition setting: unbalanced mass.</p> Full article ">Figure 21
<p>Comparison of wear results. (<b>a</b>) result of finite element calculation; (<b>b</b>) result of wear test.</p> Full article ">Figure 22
<p>Circumferential wear comparison. (<b>a</b>) serious wear; (<b>b</b>) light wear.</p> Full article ">
21 pages, 9169 KiB  
Article
Topology Analysis and Structural Optimization of Air Suspension Mechanical-Vibration-Reduction Wheels
by Xiao Meng, Xianying Feng, Peihua Liu and Xinhua Sun
Machines 2024, 12(7), 488; https://doi.org/10.3390/machines12070488 - 19 Jul 2024
Viewed by 989
Abstract
This paper designs a kind of air suspension mechanical-vibration-reduction wheel for mining engineering vehicles; the research work on topology analysis and the structural optimization of the inner and outer rims are carried out with this wheel as the research object. Using Workbench finite-element [...] Read more.
This paper designs a kind of air suspension mechanical-vibration-reduction wheel for mining engineering vehicles; the research work on topology analysis and the structural optimization of the inner and outer rims are carried out with this wheel as the research object. Using Workbench finite-element analysis software, taking the results of static analysis and modal analysis of the two as constraints, a variety of structural improvement styles are obtained through a topology analysis method and compared and verified, and a more reasonable improvement result is selected and assembled into a whole wheel for final analysis and verification. The results show that the optimization results of the wheel still meet the design’s load-bearing requirements, and the weight is lighter; the topology analysis results are ideal. Full article
(This article belongs to the Section Machine Design and Theory)
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<p>Structure of wheel parts. (<b>a</b>) Outer rim; (<b>b</b>) inner rim; (<b>c</b>) non-pneumatic tire skin; (<b>d</b>) pneumatic spring; (<b>e</b>) transverse brake; (<b>f</b>) dust shield.</p> Full article ">Figure 2
<p>Air suspension mechanical-vibration-reduction wheel.</p> Full article ">Figure 3
<p>Topology optimization analysis flowchart.</p> Full article ">Figure 4
<p>Supporting reaction force on the inner rim.</p> Full article ">Figure 5
<p>Optimization and exclusion area of the inner rim.</p> Full article ">Figure 6
<p>Topology optimization results of the inner rim. (<b>a</b>) Result 1; (<b>b</b>) result 2; (<b>c</b>) result 3.</p> Full article ">Figure 7
<p>Optimization scheme of the inner rim.</p> Full article ">Figure 8
<p>Supporting reaction force on the outer rim.</p> Full article ">Figure 9
<p>Optimization and exclusion area of the outer rim.</p> Full article ">Figure 10
<p>Topology optimization results of the outer rim. (<b>a</b>) result 1; (<b>b</b>) result 2; (<b>c</b>) result 3.</p> Full article ">Figure 11
<p>Optimization scheme of the outer rim.</p> Full article ">Figure 12
<p>Trend diagram of wheel characteristics change.</p> Full article ">Figure 13
<p>Free modal comparison of the inner rim before and after optimization.</p> Full article ">Figure 14
<p>Comparison of constraint modal of the inner rim before and after optimization.</p> Full article ">Figure 15
<p>Trend diagram of wheel characteristic change.</p> Full article ">Figure 16
<p>Free modal comparison of the outer rim before and after optimization.</p> Full article ">Figure 17
<p>Comparison of the constraint modal of the outer rim before and after optimization.</p> Full article ">Figure 18
<p>Static analysis results of the optimized wheel. (<b>a</b>) Deformation result; (<b>b</b>) stress result.</p> Full article ">
19 pages, 13096 KiB  
Article
Investigation of the Electrical Impedance Signal Behavior in Rolling Element Bearings as a New Approach for Damage Detection
by Florian Michael Becker-Dombrowsky, Johanna Schink, Julian Frischmuth and Eckhard Kirchner
Machines 2024, 12(7), 487; https://doi.org/10.3390/machines12070487 - 19 Jul 2024
Cited by 1 | Viewed by 1164
Abstract
The opportunities of impedance-based condition monitoring for rolling bearings have been shown earlier by the authors: Changes in the impedance signal and the derived features enable the detection of pitting damages. Localizing and measuring the pitting length in the raceway direction is possible. [...] Read more.
The opportunities of impedance-based condition monitoring for rolling bearings have been shown earlier by the authors: Changes in the impedance signal and the derived features enable the detection of pitting damages. Localizing and measuring the pitting length in the raceway direction is possible. Furthermore, the changes in features behavior are physically explainable. These investigations were focused on a single bearing type and only one load condition. Different bearing types and load angles were not considered yet. Thus, the impedance signals and their features of different bearing types under different load angles are investigated and compared. The signals are generated in fatigue tests on a rolling bearing test rig with conventional integrated vibration analysis based on structural borne sound. The rolling bearing impedance is gauged using an alternating current measurement bridge. Significant changes in the vibration signals mark the end of the fatigue tests. Therefore, comparing the response time of the impedance can be compared to the vibration signal response time. It can be shown that the rolling bearing impedance is an instrument for condition monitoring, independently from the bearing type. In case of pure radial loads, explicit changes in the impedance signal are detectable, which indicate a pitting damage. Under combined loads, the signal changes are detectable as well, but not as significant as under radial load. Damage-indicating signal changes occur later compared to pure radial loads, but nevertheless enable an early detection. Therefore, the rolling bearing impedance is an instrument for pitting damage detection, independently from bearing type and load angle. Full article
(This article belongs to the Special Issue Intelligent Machinery Fault Diagnosis and Maintenance)
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<p>Overview of sensing machine elements [<a href="#B17-machines-12-00487" class="html-bibr">17</a>].</p> Full article ">Figure 2
<p>Common machine elements and their equivalent electrical components [<a href="#B20-machines-12-00487" class="html-bibr">20</a>].</p> Full article ">Figure 3
<p>Electric model of the EHL contact as a function of the lubrication film thickness [<a href="#B26-machines-12-00487" class="html-bibr">26</a>].</p> Full article ">Figure 4
<p>Electric model of the EHL contact in a ball bearing [<a href="#B27-machines-12-00487" class="html-bibr">27</a>].</p> Full article ">Figure 5
<p>Test chamber of the bearing test rig.</p> Full article ">Figure 6
<p>Sectioning of a test rig chamber.</p> Full article ">Figure 7
<p>Equivalent circuit of the alternating current measurement bridge for impedance measurement [<a href="#B42-machines-12-00487" class="html-bibr">42</a>].</p> Full article ">Figure 8
<p>Data handling.</p> Full article ">Figure 9
<p>Real part, imaginary part, absolute value, and the phase angle over the operational time in hours for bearing A; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 10
<p>Real part, imaginary part, absolute value, and the phase angle over the operational time in hours for bearing J; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 11
<p>Real part and imaginary part over the operational time in hours for bearing D; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, β = 15°.</p> Full article ">Figure 12
<p>Real part and imaginary part over the operational time in hours for bearing A; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, β = 30°.</p> Full article ">Figure 13
<p>Real part and imaginary part over the operational time in hours for bearing D; bearing type FAG 7205 B XL; C/P = 1.6, n = 5000 rpm, β = 40°.</p> Full article ">Figure 14
<p>Feature F2 of real part <span class="html-italic">Re_F_2</span>, imaginary part <span class="html-italic">Im_F_2</span>, absolute value <span class="html-italic">abs_F_2,</span> and the phase angle <span class="html-italic">phase_F_2</span> over the measurements for bearing A (<b>left</b>) and bearing J (<b>right</b>); bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 15
<p>Feature F3 of real part Re_F_3, imaginary part Im_F_3, absolute value abs_F_3, and the phase angle phase_F_3 over the measurements for bearing A (<b>left</b>) and bearing J (<b>right</b>); bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 16
<p>Feature F4 of real part Re_F_4, imaginary part Im_F_4, absolute value abs_F_4, and the phase angle phase_F_4 over the measurements for bearing A (<b>left</b>) and bearing J (<b>right</b>); bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 17
<p>Feature F2 of real part <span class="html-italic">Re_F_2</span>, imaginary part <span class="html-italic">Im_F_2</span>, absolute value <span class="html-italic">abs_F_2,</span> and the phase angle <span class="html-italic">phase_F_2</span> over the measurements; bearing type FAG 7205 B XL; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mn>4410</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>3700</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 40°.</p> Full article ">Figure 18
<p>Amplitudes and their corresponding order on the cumulative damage for bearing A; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 19
<p>Maximum value of the interval of order 5.43 for bearing A; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 20
<p>Maximum value of the interval of order 5.43 for bearing C; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 21
<p>Natural logarithm of the maximum value of the interval of order 5.43 for bearing A; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">Figure 22
<p>Real gradient of the natural logarithm of the maximum value of the interval of order 5.43 for bearing A; bearing type FAG 6205 C C3; C/P = 1.6, n = 5000 rpm, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>r</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mn>9375</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo> </mo> <mi mathvariant="normal">N</mi> </mrow> </semantics></math>, β = 0°.</p> Full article ">
13 pages, 1723 KiB  
Article
The Coupled Wing Morphing of Ornithopters Improves Attitude Control and Agile Flight
by Yu Cai, Guangfa Su, Jiannan Zhao and Shuang Feng
Machines 2024, 12(7), 486; https://doi.org/10.3390/machines12070486 - 19 Jul 2024
Viewed by 1300
Abstract
Bird wings are exquisite mechanisms integrated with multiple morphological deformation joints. The larger avian species are particularly adept at utilizing their wings’ flapping, folding, and twisting motions to control the wing angle and area. These motions mainly involve different types of spanwise folding [...] Read more.
Bird wings are exquisite mechanisms integrated with multiple morphological deformation joints. The larger avian species are particularly adept at utilizing their wings’ flapping, folding, and twisting motions to control the wing angle and area. These motions mainly involve different types of spanwise folding and chordwise twisting. It is wondered whether the agile maneuverability of birds is based on the complex coupling of these wing morphing changes. To investigate this issue, we designed a two-section wing structure ornithopter capable of simultaneously controlling both spanwise folding and chordwise twisting and applied it to research on heading control. The experimental data collected from outdoor flights describe the differing flight capabilities between the conventional and two-section active twist wing states, indicating that incorporating an active twist structure enhances the agility and maneuverability of this novel flapping aircraft. In the experiments on yaw control, we observed some peculiar phenomena: although the twisting motion of the active twist ornithopter wings resembles that of a fixed-wing aileron control, due to the intricate coupling of the wing flapping and folding, the ornithopter, under the control of active twist structures, exhibited a yaw direction opposite to the expected direction (directly applying the logic assumed by the fixed-wing aileron control). Addressing this specific phenomenon, we provide a plausible model explanation. In summary, our study with active twist mechanisms on ornithopters corroborates the positive impact of active deformation on their attitude agility, which is beneficial for the design of similar bio-inspired aircraft in the future. Full article
(This article belongs to the Special Issue Advances and Applications in Unmanned Aerial Vehicles)
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<p>Geometric morphology of bird wing motion, where the first row depicts simplified schematic diagrams of motion, and the second row shows corresponding bird flight forms. (<b>a</b>) The up-and-down flapping of the wings without static geometric deformation. (<b>b</b>) Spanwise extension–retraction of the wings. (<b>c</b>) Spanwise folding of the wings, folding upwards and downwards. (<b>d</b>) Chordwise twisting of the wings. (<b>e</b>) Flapping flight mode, exemplified by hummingbirds, where the wings do not undergo static deformation. (<b>f</b>) Spanwise extension–retraction flight mode, exemplified by peregrine falcons. (<b>g</b>) Spanwise folding flight mode, represented by albatrosses with large wing spans. (<b>h</b>) Flight mode of pigeons, enhancing agility through chordwise twisting during flight [<a href="#B8-machines-12-00486" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) Skeletal structure of an ornithopter. The power system motor and variable speed gears form a set of variable speed systems. The left wing has a three-stage transmission, and the right wing has a four-stage transmission. (<b>b</b>) Diagram of the effect of the twisted wing structure in action (with fuselage coordinates, originating at the center of mass of the ornithopter). (<b>c</b>) Twisted wing structure, a four-bar linkage servo arm (blue), linkage on the wing spar (green), connecting bar (light blue), and virtual linkage between wing spar and servo arm root. (<b>d</b>) Diagram of the change in the center position of the flapping wing craft with the flapping of the wings. The blue color is for a two-section wing flap ornithopter, and the red color is for a single-ended wing flap of the same size. (<b>e</b>) Definition of the coordinates of the tail–fuselage junction.</p> Full article ">Figure 3
<p>(<b>a</b>) Experimental force analysis of ornithopters under ANIPRO RL4 turntable system allows for the measurement of the lift and thrust forces experienced by the ornithopters under specific conditions, accompanied by a dynamic capture system to measure the flapping frequency of the ornithopters. (<b>b</b>) The relationship between flapping frequency and thrust coefficient (<math display="inline"><semantics> <msub> <mi>C</mi> <mi>T</mi> </msub> </semantics></math>) of ornithopters at different airspeeds. (<b>c</b>) The relationship between relative airspeed and lift coefficient (<math display="inline"><semantics> <msub> <mi>C</mi> <mi>L</mi> </msub> </semantics></math>) of the ornithopter at various flapping frequencies.</p> Full article ">Figure 4
<p>(<b>a</b>,<b>b</b>) The outdoor flight experiments of two-section wing ornithopter.</p> Full article ">Figure 5
<p>Graph of the relationship between the attitude of an ornithopter and control signals. (<b>a</b>) Roll angle and tail wing Z-axis control signal for the ornithopter. (<b>b</b>) The linear regression plot of roll angle against Z-axis control signal. (<b>c</b>) Pitch angle and tail wing Y-axis control signal for the ornithopter. (<b>d</b>) The linear regression plot of pitch angle against the Y-axis control signal.</p> Full article ">Figure 6
<p>The coupling relationship diagram between pitch angle and roll angle under tail control.</p> Full article ">Figure 7
<p>The control relationship between the wing twist control signal and the aircraft’s roll and yaw angles. (<b>a</b>) The twist wing signal’s anticipated control effect on the ornithopter’s roll angle (similar to the aileron’s control effect on fixed-wing aircraft). (<b>b</b>) The twist wing signal’s actual control effect on the ornithopter’s roll angle is completely opposite to the anticipated effect. (<b>c</b>) The coupling relationship between the yaw and roll angles during actual flight. (<b>d</b>) The coupling relationship between the yaw angle signal filtered by low frequency and the roll angle. (<b>e</b>) The linear regression curve between the wing twist signal and the roll angle.</p> Full article ">Figure 8
<p>Comparison diagram between fixed-wing aircraft and two-section wing ornithopter. (<b>a</b>) Schematic illustration of aileron twisting in fixed-wing aircraft. (<b>b</b>) Schematic illustration of outer wing twisting in two-section wing ornithopter, along with its corresponding force analysis.</p> Full article ">
22 pages, 1068 KiB  
Article
Integrated Control of a Wheel–Track Hybrid Vehicle Based on Adaptive Model Predictive Control
by Boyuan Li, Zheng Pan, Junhua Liu, Shiyu Zhou, Shaoxun Liu, Shouyuan Chen and Rongrong Wang
Machines 2024, 12(7), 485; https://doi.org/10.3390/machines12070485 - 19 Jul 2024
Viewed by 935
Abstract
Hybrid wheel–track systems have found extensive applications due to the advantages a combination of wheels and tracks. However, the coupling influence between the wheeled and tracked mechanisms poses a challenge to stable and efficient controller design and implementation. This paper focuses on the [...] Read more.
Hybrid wheel–track systems have found extensive applications due to the advantages a combination of wheels and tracks. However, the coupling influence between the wheeled and tracked mechanisms poses a challenge to stable and efficient controller design and implementation. This paper focuses on the lateral dynamic control of a vehicle in scenarios where both tracks and wheels are in contact with the ground. A dynamic model of a vehicle is first established based on the tire brush model and linearized general track model. Based on the dynamic model, a novel adaptive model predictive control (AMPC) method is designed considering the coupling and nonlinearity of the wheels and tracks to simultaneously regulate both mechanisms. Compared with traditional model predictive control approaches, the AMPC controller takes the side-slip angle and slip ratio as constraints to prevent the vehicle from reaching unstable states. Simulations are conducted to validate the effectiveness of the controller, and the results indicate that the controller has the capacity to optimize the objective’s yaw-rate response while maintaining lateral vehicle stability and preventing slip by imposing constraints. Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicles Dynamics and Control)
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<p>The structure of the vehicle model.</p> Full article ">Figure 2
<p>The structure of the vehicle with tracks on both sides of the vehicle body. Note that <math display="inline"><semantics> <msub> <mi>δ</mi> <mi>r</mi> </msub> </semantics></math> is negative in this figure.</p> Full article ">Figure 3
<p>Kinematics of the elements on the track. The points on the graph are labeled with coordinates adjacent to each point, using font colors that match the respective coordinate systems, indicating the coordinates of each point in their corresponding coordinate system.</p> Full article ">Figure 4
<p>Structure of the controller. Re: I have revised.</p> Full article ">Figure 5
<p>Structure of the dynamic model used for simulation.</p> Full article ">Figure 6
<p>Desired and actual yaw rates in the dual J-turn simulation.</p> Full article ">Figure 7
<p>Longitudinal speed of the vehicle.</p> Full article ">Figure 8
<p>Steering angle of the vehicle.</p> Full article ">Figure 9
<p>Wheel driving torque in the dual J-turn simulation.</p> Full article ">Figure 10
<p>Track driving torque in the dual J-turn simulation.</p> Full article ">Figure 11
<p>Wheel rotational speed.</p> Full article ">Figure 12
<p>Track rotational speed.</p> Full article ">Figure 13
<p>Vehicle CG slip angle.</p> Full article ">
33 pages, 6608 KiB  
Review
A Review of the Intelligent Condition Monitoring of Rolling Element Bearings
by Vigneshwar Kannan, Tieling Zhang and Huaizhong Li
Machines 2024, 12(7), 484; https://doi.org/10.3390/machines12070484 - 18 Jul 2024
Cited by 1 | Viewed by 2270
Abstract
Bearing component damage contributes significantly to rotating machinery failures. It is vital for the rotor-bearing system to be in good condition to ensure the proper functioning of the machine. Over recent decades, extensive research has been devoted to the condition monitoring of rotational [...] Read more.
Bearing component damage contributes significantly to rotating machinery failures. It is vital for the rotor-bearing system to be in good condition to ensure the proper functioning of the machine. Over recent decades, extensive research has been devoted to the condition monitoring of rotational machinery, with a particular focus on bearing health. This paper provides a comprehensive literature review of recent advancements in intelligent condition monitoring technologies for rolling element bearings. Fundamental monitoring strategies are introduced, covering various sensing, signal processing, and feature extraction techniques for detecting defects in rolling element bearings. While vibration-based monitoring remains prevalent, alternative sensor types are also explored, offering complementary diagnostic capabilities or detecting different defect types compared to accelerometers alone. Signal processing and feature extraction techniques, including time domain, frequency domain, and time–frequency domain analysis, are discussed for their ability to provide diverse perspectives for signal representation, revealing unique insights relevant to condition monitoring. Special attention is given to information fusion methodologies and the application of intelligent algorithms. Multisensor systems, whether homogeneous or heterogeneous, integrated with information fusion techniques hold promise in enhancing accuracy and reliability by overcoming limitations associated with single-sensor monitoring. Furthermore, the adoption of AI techniques, such as machine learning, metaheuristic optimisation, and deep-learning methods, has led to significant advancements in condition monitoring, yielding successful outcomes with improved accuracy and robustness in various studies. Finally, avenues for further advancements to improve monitoring accuracy and reliability are identified, offering insights into future research directions. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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<p>(<b>a</b>) Bearing geometry and impact signal ([<a href="#B15-machines-12-00484" class="html-bibr">15</a>]); (<b>b</b>) Typical envelope signatures due to defects in outer race, inner race, and a rolling element ([<a href="#B17-machines-12-00484" class="html-bibr">17</a>]).</p> Full article ">Figure 2
<p>A taxonomy of the most common signal processing and feature extraction methods (adapted from [<a href="#B50-machines-12-00484" class="html-bibr">50</a>].</p> Full article ">Figure 3
<p>HFRT procedure (from [<a href="#B19-machines-12-00484" class="html-bibr">19</a>]).</p> Full article ">Figure 4
<p>Schematic diagram of the product envelope spectrum.</p> Full article ">Figure 5
<p>The framework of a knowledge-informed deep network with feature extraction and fusion modules [<a href="#B94-machines-12-00484" class="html-bibr">94</a>].</p> Full article ">Figure 6
<p>(<b>a</b>) The general structure of an ANN; (<b>b</b>) Principle of SVM: Segregation of two classes in two-dimensional space with an optimally placed hyperplane; (<b>c</b>) Decision tree schematic.</p> Full article ">Figure 7
<p>Frame of procedures of the proposed diagnosis method for transfer learning [<a href="#B114-machines-12-00484" class="html-bibr">114</a>].</p> Full article ">Figure 8
<p>Overview of a vanilla transformer architecture [<a href="#B115-machines-12-00484" class="html-bibr">115</a>].</p> Full article ">
26 pages, 6952 KiB  
Article
Maintainability Assessment during the Design Phase: Integrating MTA and UNE 151001
by Franco Donaire, Orlando Durán, José Ignacio Vergara and Adolfo Arata
Machines 2024, 12(7), 483; https://doi.org/10.3390/machines12070483 - 17 Jul 2024
Viewed by 979
Abstract
The focus on maintenance actions in the early design phases has been a trend in recent years. The main sources of information during the design of the maintainability process include operator reports, maintainer experience, failure history, and manufacturer recommendations. During this process, an [...] Read more.
The focus on maintenance actions in the early design phases has been a trend in recent years. The main sources of information during the design of the maintainability process include operator reports, maintainer experience, failure history, and manufacturer recommendations. During this process, an important aspect is related to the configuration of maintenance tasks and interventions, such as their main phases, activities, and durations. The allocation or estimation of maintainability involves identifying and/or estimating the mean time to repair (MTTR) for each component or system. The time of the maintenance tasks or the repair time are fundamental for companies, as the availability of equipment directly depends on this parameter. In this study, a new method for evaluating maintainability during the design phases is proposed. The method is based on the integration of the maintenance task analysis (MTA) principles and the UNE 151001 maintainability evaluation standard. A data structure is proposed that serves the application of the UNE151001 procedures, obtaining a data-based maintainability evaluation. As a validation procedure, an application of the proposed approach is presented using two overhead cranes. Comparisons and recommendations are made regarding the maintainability of both pieces of equipment. Finally, some managerial and engineering insights are presented. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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<p>Maintainability internal and external influencing factors.</p> Full article ">Figure 2
<p>Proposed methodology.</p> Full article ">Figure 3
<p>Proposed data structure.</p> Full article ">Figure 4
<p>Equipment A: 25-ton gantry-type overhead crane.</p> Full article ">Figure 5
<p>Equipment B: 13.5-ton overhead crane.</p> Full article ">Figure 6
<p>Maintainability indicator for the steel cable replacement on Equipment A.</p> Full article ">Figure 7
<p>Maintainability indicator for the steel cable replacement on Equipment B.</p> Full article ">Figure 8
<p>Task time, change of steel rope cable on Equipment A and B.</p> Full article ">Figure 9
<p>Breakdown of the motor and magnetic brake.</p> Full article ">Figure 10
<p>(<b>a</b>,<b>b</b>) Motion transmission system. (1: wheel; 2: reduction box; 3: motor; 4: brake).</p> Full article ">Figure 11
<p>Maintainability indicator for the task of inspection and adjustment of trolley translation brakes in Equipment A.</p> Full article ">Figure 12
<p>Maintainability indicator for the task of inspection and adjustment of trolley translation brakes in equipment B.</p> Full article ">Figure 13
<p>Task time, inspection, and adjustment of brakes for trolley movement in Equipment A and B.</p> Full article ">Figure 14
<p>Festoon cable and support structure.</p> Full article ">Figure 15
<p>Maintainability indicator, replacement of rails and festoon trolleys in Equipment A.</p> Full article ">Figure 16
<p>Maintainability indicator, replacement of rails and festoon trolleys in Equipment B.</p> Full article ">Figure 17
<p>Task time, replacement of rails and festoon trolleys in Equipment A and B.</p> Full article ">Figure 18
<p>Maintainability indicator for replacement of end-of-travel limiter on the crane in Equipment A.</p> Full article ">Figure 19
<p>Maintainability indicator for replacement of end-of-travel limiter on the crane in Equipment B.</p> Full article ">Figure 20
<p>Task time, replacement of bridge travel limiter in Equipment A and B.</p> Full article ">
24 pages, 7739 KiB  
Article
MT-SIPP: An Efficient Collision-Free Multi-Chain Robot Path Planning Algorithm
by Jinchao Miao, Ping Li, Chuangye Chen, Jiya Tian and Liwei Yang
Machines 2024, 12(7), 482; https://doi.org/10.3390/machines12070482 - 17 Jul 2024
Viewed by 900
Abstract
Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we [...] Read more.
Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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<p>Diagram of conflicts. (<b>a</b>) point conflict; (<b>b</b>) edge conflict.</p> Full article ">Figure 2
<p>Point conflict of multi-train robot.</p> Full article ">Figure 3
<p>Schematic of grid security interval update.</p> Full article ">Figure 4
<p>Grid safety interval update under k robust planning (<math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>).</p> Full article ">Figure 5
<p>Schematic diagram of train collision. (<b>a</b>) Status 1; (<b>b</b>) Status 2.</p> Full article ">Figure 6
<p>Schematic diagram of the improved robust method for resolving train travel conflicts.</p> Full article ">Figure 7
<p>Schematic diagram of train waiting.</p> Full article ">Figure 8
<p>Time step adjustment of the car body occupying the grid when the train front is waiting.</p> Full article ">Figure 9
<p>Three situations in which train stop conflicts occur. (<b>a</b>) Status 1; (<b>b</b>) Status 2; (<b>c</b>) Status 3.</p> Full article ">Figure 10
<p>Update of the safety interval of the car body occupying the grid after the train stops.</p> Full article ">Figure 11
<p>MT-SIPP algorithm block diagram.</p> Full article ">Figure 12
<p>Empty map Empty-48-48.</p> Full article ">Figure 13
<p>Comparison of success rates of several algorithms in blank map environment. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Random map random-32-32-20.</p> Full article ">Figure 15
<p>Comparison of solving success rates of several algorithms in a random map environment. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 15 Cont.
<p>Comparison of solving success rates of several algorithms in a random map environment. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Room map Room-32-32-4.</p> Full article ">Figure 17
<p>Comparison of the success rates of several algorithms in the room map environment. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 18
<p>Path planning solutions by our MT-SIPP algorithm. (<b>a</b>) empty-48-48 <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mi>n</mi> <mo>=</mo> <mn>40</mn> <mo>,</mo> <mi>k</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>); (<b>b</b>) random-32-32-20 (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>30</mn> <mo>,</mo> <mo> </mo> <mi>k</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>); (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>room</mi> <mo>−</mo> <mn>32</mn> <mo>−</mo> <mn>32</mn> <mo>−</mo> <mn>4</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <mi>n</mi> <mo>=</mo> <mn>30</mn> <mo>,</mo> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>).</p> Full article ">
33 pages, 23835 KiB  
Article
Study on Rubbing-Induced Vibration Characteristics Considering the Flexibility of Coated Casings and Blades
by Yong Zhang, Shuhua Yang, Xingyu Tai, Hui Ma, Hong Guan, Qinqin Mu, Lin Qu and Xiangfu Ding
Machines 2024, 12(7), 481; https://doi.org/10.3390/machines12070481 - 17 Jul 2024
Cited by 1 | Viewed by 852
Abstract
Rubbing between a blade and its coated casing is one of the main failures in aero-engine systems. This paper aims to study the effects of coated casings on rubbing-induced dynamic responses considering the flexibility of the coated casing and the flexibility of the [...] Read more.
Rubbing between a blade and its coated casing is one of the main failures in aero-engine systems. This paper aims to study the effects of coated casings on rubbing-induced dynamic responses considering the flexibility of the coated casing and the flexibility of the blade. Firstly, an actual compressor blade is established by the shell element and verified by the experiment and ANSYS 19.2 software. Subsequently, a new dynamic model for the coated casing is proposed based on the laminated shell element, and the proposed dynamic model for the coated casing is verified by comparing the natural characteristics calculated by ANSYS software. Moreover, a comprehensive analysis is conducted to analyze the influences of the casing model, coating parameters, and casing parameters on vibration characteristics. Finally, the results show that the coating can diminish the severity level of rubbing. Notably, the material and thickness of the coating can change the nodal diameter vibrations of the casings (NDVCs) induced by rubbing. This study provides valuable guidance for the optimization and design of blade–casing systems. Full article
(This article belongs to the Topic Nonlinear Phenomena, Chaos, Control and Applications to Engineering and Science and Experimental Aspects of Complex Systems)
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<p>Schematic of a blade–casing system and the hazards induced by rubbing: (<b>a</b>) blade-coated casing system, (<b>b</b>) blade damage in Ref. [<a href="#B4-machines-12-00481" class="html-bibr">4</a>]; (<b>c</b>) coating damage in Ref. [<a href="#B5-machines-12-00481" class="html-bibr">5</a>].</p> Full article ">Figure 2
<p>Schematic of the flexible blade–casing system.</p> Full article ">Figure 3
<p>Schematic of rotating blades and FE model: (<b>a</b>) schematic; (<b>b</b>) FE model.</p> Full article ">Figure 4
<p>Test rig for measuring natural frequencies [<a href="#B31-machines-12-00481" class="html-bibr">31</a>]: (<b>a</b>) test bench; (<b>b</b>) mobile workstation; (<b>c</b>) DH5956 data-collecting and -processing system.</p> Full article ">Figure 5
<p>Study of the influence of parametric errors: (<b>a</b>) the influence of density, (<b>b</b>) the influence of elasticity modulus.</p> Full article ">Figure 6
<p>Schematic of laminated shell elements: (<b>a</b>) shell element; (<b>b</b>) lamination diagram.</p> Full article ">Figure 7
<p>Mode shapes of the first four modes of the coated casing (the first row comprises the mode shapes from the ANSYS software, and the second row comprises the mode shapes from the proposed model).</p> Full article ">Figure 8
<p>FE model of the coated casing with elastic support.</p> Full article ">Figure 9
<p>Schematic of rubbing model [<a href="#B79-machines-12-00481" class="html-bibr">79</a>]: (<b>a</b>) schematic of blade–casing rubbing; (<b>b</b>) blade–casing rubbing model; (<b>c</b>) partial enlarged drawing.</p> Full article ">Figure 10
<p>Schematic of the hysteretic contact-force models: (<b>a</b>) blade-coated casing system; (<b>b</b>) equivalent contact diagram.</p> Full article ">Figure 11
<p>NRFs and vibration responses of blades for various casing models: (<b>a</b>) NRF; (<b>b</b>) TDW of radial displacement; (<b>c</b>) TDW of bending displacement; (<b>d</b>) spectrum.</p> Full article ">Figure 12
<p>Vibration responses of different nodes at blade tips: (<b>a1</b>,<b>b1</b>) radial displacement; (<b>a2</b>,<b>b2</b>) bending displacement; (<b>c1</b>,<b>c2</b>) spectrum. The first row shows the response at the 11th node of the blade tip. The first row shows the response at the 1st node of the blade tip.</p> Full article ">Figure 13
<p>Deformation diagrams of the casing at different moments (unit: m): (<b>a</b>) <span class="html-italic">t</span>=0.324 s, corresponding to point A; (<b>b</b>) <span class="html-italic">t</span>=0.3255 s, corresponding to point B; (<b>c</b>) <span class="html-italic">t</span>=0.3247 s, corresponding to point C; (<b>d</b>) <span class="html-italic">t</span> = 0.3285 s, corresponding to point D.</p> Full article ">Figure 14
<p>Vibration response of rigid casing: (<b>a</b>) time-domain waveform; (<b>b</b>) spectrum.</p> Full article ">Figure 15
<p>Vibration response of rubbing point A with the flexible casing: (<b>a</b>) time-domain waveform; (<b>b</b>) spectrum.</p> Full article ">Figure 16
<p>Vibration responses of rubbing points B, C, and D with flexible casing: (<b>a1</b>,<b>a2</b>) point B; (<b>b1</b>,<b>b2</b>) point C; (<b>c1</b>,<b>c2</b>) point D.</p> Full article ">Figure 17
<p>Normal rubbing forces and vibration responses at various coating thicknesses: (<b>a</b>) NRF; (<b>b</b>) TDW of radial displacement; (<b>c</b>) TDW of bending displacement.</p> Full article ">Figure 18
<p>Deformation diagrams of the casing at different moments (unit: m): (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.324</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.3255</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.3247</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.3285</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 19
<p>Dynamic responses of casings at various coating thicknesses: (<b>a</b>) TDW of displacement at point A; (<b>b</b>) displacement spectrum at point A; (<b>c</b>) TDW of displacement at point B; (<b>d</b>) displacement spectrum at point B.</p> Full article ">Figure 20
<p>Normal rubbing forces and dynamic responses of blades with various coating materials: (<b>a</b>) normal rubbing force; (<b>b</b>) bending displacement of the 21st node of the blade tip.</p> Full article ">Figure 21
<p>Deformation diagrams of the casing at different moments: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.324</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.3255</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.3247</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0.3285</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 22
<p>Dynamic responses of casings with various coating materials: (<b>a</b>) TDW of displacement at point A; (<b>b</b>) displacement spectrum at point A; (<b>c</b>) TDW of displacement at point B; (<b>d</b>) displacement spectrum at point B.</p> Full article ">Figure 23
<p>Equivalent stiffness of rubbing and NRF with different spring stiffnesses: (<b>a</b>) equivalent radial stiffness of the coated casing; (<b>b</b>) influence of radial support stiffness; (<b>c</b>) influence of tangential support stiffness; (<b>d</b>) influence of axial support stiffness.</p> Full article ">Figure 24
<p>Vibration responses of the blade with different radial spring stiffnesses: (<b>a</b>) TDW of bending displacement; (<b>b</b>) spectrum cascades of bending displacement.</p> Full article ">Figure 25
<p>Dynamic responses of point A with various spring stiffnesses: (<b>a1</b>,<b>a2</b>) influence of radial support stiffness; (<b>b1</b>,<b>b2</b>) influence of tangential support stiffness; (<b>c1</b>,<b>c2</b>) influence of axial support stiffness. The <b>left</b> side of the figure shows the TDW of the displacement and the <b>right</b> shows the spectrum.</p> Full article ">Figure 26
<p>Vibration responses of blade–casing rubbing with different casing lengths: (<b>a1</b>,<b>a2</b>) 21st node of the blade tip; (<b>b1</b>,<b>b2</b>) point A of the casing.</p> Full article ">Figure A1
<p>Schematic of the degenerated shell element is displayed [<a href="#B80-machines-12-00481" class="html-bibr">80</a>]. The element is degraded from a 3D solid element and has some of the properties of a solid element, enclosed by two surfaces above and below and a wraparound surface bushed by a straight line in the direction of shell thickness. It has eight nodes. <math display="inline"><semantics> <mi>ξ</mi> </semantics></math> and <math display="inline"><semantics> <mi>η</mi> </semantics></math> are the surface coordinates of the shell, and <math display="inline"><semantics> <mi>ζ</mi> </semantics></math> is the linear coordinate in the thickness direction.</p> Full article ">
25 pages, 9226 KiB  
Article
Development of Standalone Extended-Reality-Supported Interactive Industrial Robot Programming System
by Andrija Devic, Jelena Vidakovic and Nikola Zivkovic
Machines 2024, 12(7), 480; https://doi.org/10.3390/machines12070480 - 17 Jul 2024
Viewed by 1193
Abstract
Extended reality (XR) is one of the most important technologies in developing a new generation of human–machine interfaces (HMIs). In this study, the design and implementation of a standalone interactive XR-supported industrial robot programming system using the Unity game engine is presented. The [...] Read more.
Extended reality (XR) is one of the most important technologies in developing a new generation of human–machine interfaces (HMIs). In this study, the design and implementation of a standalone interactive XR-supported industrial robot programming system using the Unity game engine is presented. The presented research aims to achieve a cross-platform solution that enables novel tools for robot programming, trajectory validation, and robot programming debugging within an extended reality environment. From a robotics perspective, key design tasks include modeling in the Unity environment based on robot CAD models and control design, which include inverse kinematics solution, trajectory planner development, and motion controller set-up. Furthermore, the integration of real-time vision, touchscreen interaction, and AR/VR headset interaction are involved within the overall system development. A comprehensive approach to integrating Unity with established industrial robot modeling conventions and control strategies is presented. The proposed modeling, control, and programming concepts, procedures, and algorithms are verified using a 6DoF robot with revolute joints. The benefits and challenges of using a standalone XR-supported interactive industrial robot programming system compared to integrated Unity–robotics development frameworks are discussed. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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<p>XR-assisted HMI applications in industrial robotics: Robot programming [<a href="#B1-machines-12-00480" class="html-bibr">1</a>,<a href="#B2-machines-12-00480" class="html-bibr">2</a>,<a href="#B4-machines-12-00480" class="html-bibr">4</a>,<a href="#B7-machines-12-00480" class="html-bibr">7</a>,<a href="#B8-machines-12-00480" class="html-bibr">8</a>,<a href="#B9-machines-12-00480" class="html-bibr">9</a>,<a href="#B10-machines-12-00480" class="html-bibr">10</a>,<a href="#B11-machines-12-00480" class="html-bibr">11</a>,<a href="#B12-machines-12-00480" class="html-bibr">12</a>,<a href="#B13-machines-12-00480" class="html-bibr">13</a>,<a href="#B14-machines-12-00480" class="html-bibr">14</a>,<a href="#B15-machines-12-00480" class="html-bibr">15</a>,<a href="#B16-machines-12-00480" class="html-bibr">16</a>,<a href="#B17-machines-12-00480" class="html-bibr">17</a>]; System engineering-Design of robotic cells [<a href="#B21-machines-12-00480" class="html-bibr">21</a>,<a href="#B22-machines-12-00480" class="html-bibr">22</a>], System engineering—Human factors research [<a href="#B23-machines-12-00480" class="html-bibr">23</a>,<a href="#B24-machines-12-00480" class="html-bibr">24</a>,<a href="#B25-machines-12-00480" class="html-bibr">25</a>,<a href="#B26-machines-12-00480" class="html-bibr">26</a>]; Personnel training [<a href="#B27-machines-12-00480" class="html-bibr">27</a>,<a href="#B28-machines-12-00480" class="html-bibr">28</a>].</p> Full article ">Figure 2
<p>System architecture for the proposed XR-assisted robot programming framework.</p> Full article ">Figure 3
<p>6DoF robot RL15: (<b>a</b>) in laboratory setting; (<b>b</b>) 3D model designed in SolidWorks; (<b>c</b>) Unity model.</p> Full article ">Figure 4
<p>(<b>a</b>) BASE_IK frame; (<b>b</b>) selected BASE_U frame.</p> Full article ">Figure 5
<p>The GUI of the developed AR/VR−enhanced robot programming application.</p> Full article ">Figure 6
<p>GUI buttons for AR/VR−LT programming method: (<b>a</b>) AR environment; (<b>b</b>) VR environment.</p> Full article ">Figure 7
<p>Robot programming by AR/VR−WT method: (<b>a</b>) AR environment; (<b>b</b>) VR environment.</p> Full article ">Figure 8
<p>AR/VR−PbD programming method: (<b>a</b>) AR environment; (<b>b</b>) VR environment.</p> Full article ">Figure 9
<p>Trajectory validation using AR/VR−S: (<b>a</b>) AR environment; (<b>b</b>) VR environment.</p> Full article ">Figure 10
<p>AR/VR Debugger Tool (AR/VR−D) GUI: (<b>a</b>) an example of unattainable TCP path; (<b>b</b>) correcting unachievable TCP trajectory in EDIT mode.</p> Full article ">Figure 11
<p>Developed standalone XR-assisted robot programming and verification tools.</p> Full article ">Figure 12
<p>General system flow chart of the proposed cross-platform system.</p> Full article ">Figure 13
<p>Flow chart of developed AR/VR-WT.</p> Full article ">Figure 14
<p>Flow charts of the other developed AR/VR functionalities: (<b>a</b>) AddCoords; (<b>b</b>) EditCoords; (<b>c</b>) LoadCoords; (<b>d</b>) RemoveCoords; (<b>e</b>) SaveCoords.</p> Full article ">Figure A1
<p>Change of (<b>a</b>) position, (<b>b</b>) speed, and (<b>c</b>) acceleration for the normalized coordinate π.</p> Full article ">
20 pages, 3921 KiB  
Article
Design of Mixing Device Shafts Based on a Proposed Calculation Method Supported by Finite Element Method Analysis
by Luminita Bibire, Alexandra-Dana Chitimus and Vlad Ciubotariu
Machines 2024, 12(7), 479; https://doi.org/10.3390/machines12070479 - 16 Jul 2024
Viewed by 774
Abstract
The elasticity of bearings as well as their clearance have an essential influence on the total arrow and, therefore, on their own pulsation. In most of the literature, this elasticity is neglected in the calculation of shaft deflections. In some work, the elasticity [...] Read more.
The elasticity of bearings as well as their clearance have an essential influence on the total arrow and, therefore, on their own pulsation. In most of the literature, this elasticity is neglected in the calculation of shaft deflections. In some work, the elasticity of the bearings has been taken into account when calculating the deflection of the mixing device shaft, but this has been carried out on the basis of a high degree of customization: the behavior of the bearings has been considered linearly elastic, which does not correspond to reality because according to the elastic response of the bearing, it is a nonlinear function of the radial displacement. When the shaft of a mixing device operates in a pressure vessel, at the outlet of the pressure vessel, the shaft is provided with a sealing device, which can be considered a third bearing. This aspect is also not taken into account in the calculation of the shaft’s deflection, which leads to a certain degree of error in its determination. This study aims to highlight the influence of the elasticity of the bearings and the sealing device on the stiffness of the shaft and to propose a method that supports a calculation program for calculating the elastic line of a vertical cantilever shaft, considering the role played by the bearings in the case that they behave nonlinearly and the sealing device as the third bearing. This problem was solved both by applying our own method and with the help of the FEM. Full article
(This article belongs to the Special Issue Selected Papers from OPROTEH 2023: 18th International Conference of Constructive Design and Technological Optimization in Machine Building)
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<p>The shaft of a mixing device with a vertical cantilever and without a sealing device (AFDE).</p> Full article ">Figure 2
<p>The shaft of a mixing device with a vertical cantilever and sealing device (ACDE).</p> Full article ">Figure 3
<p>Hypotheses for determining shaft deformations, where the lunations and rotations of the planes normal to the shaft axis, which delimit a volume element, are neglected.</p> Full article ">Figure 4
<p>Logical scheme.</p> Full article ">Figure 5
<p>Influence of turation on the arrow for different working environments, considering bearings as rigid supports (RR)—classical method: (<b>a</b>) air; (<b>b</b>) water; and (<b>c</b>) oil.</p> Full article ">Figure 6
<p>Influence of turation on the arrow considering elastic supports (bearings, RU) for different working environments: (<b>a</b>) air; (<b>b</b>) water; and (<b>c</b>) oil.</p> Full article ">Figure 7
<p>Influence of turation on the arrow, considering the sealing device as a third support (DE), for different working environments: (<b>a</b>) air; (<b>b</b>) water; and (<b>c</b>) oil.</p> Full article ">Figure 8
<p>Influence of turation on the arrow for the same working environment and different support systems: (<b>a</b>) 400 rpm; (<b>b</b>) 640 rpm; and (<b>c</b>) 1008 rpm.</p> Full article ">Figure 9
<p>Influence of turation on the arrow, considering the elastic supports (bearings, RU), for different working environments (own method–Cmmp method): (<b>a</b>) air; (<b>b</b>) water; and (<b>c</b>) oil.</p> Full article ">Figure 10
<p>Influence of turation on the arrow, considering elastic supports (bearings, RU), for different working environments (own method—Newton method): (<b>a</b>) air; (<b>b</b>) water; and (<b>c</b>) oil.</p> Full article ">Figure 11
<p>Influence of turation on the arrow, considering the sealing device as a third support (DE), for different working environments (own method–Cmmp method): (<b>a</b>) air; (<b>b</b>) water; and (<b>c</b>) oil.</p> Full article ">Figure 12
<p>Influence of turation on the arrow, considering the sealing device as a third support (DE), for different working environments (own method—Newton method): (<b>a</b>) air; (<b>b</b>) water; and (<b>c</b>) oil.</p> Full article ">Figure 13
<p>Influence of turation on the arrow for the same working environment (Own method–Cmmp method) and different support systems: (<b>a</b>) 400 rpm; (<b>b</b>) 640 rpm; and (<b>c</b>) 1008 rpm.</p> Full article ">Figure 14
<p>Influence of turation on the arrow for the same working environment (own method—Newton method) and different support systems: (<b>a</b>) 400 rpm; (<b>b</b>) 640 rpm; and (<b>c</b>) 1008 rpm.</p> Full article ">Figure 15
<p>Hypotheses for determining shaft deformations where the lunation and rotations of the planes normal to the shaft axis, which delimit a volume element, are neglected.</p> Full article ">
26 pages, 21888 KiB  
Article
The Control of Handling Stability for Four-Wheel Steering Distributed Drive Electric Vehicles Based on a Phase Plane Analysis
by Guanfeng Wang and Qiang Song
Machines 2024, 12(7), 478; https://doi.org/10.3390/machines12070478 - 16 Jul 2024
Viewed by 1102
Abstract
For the sake of enhancing the handling and stability of distributed drive electric vehicles (DDEVs) under four-wheel steering (4WS) conditions, this study proposes a novel hierarchical control strategy based on a phase plane analysis. This approach involves a meticulous comparison of the stable [...] Read more.
For the sake of enhancing the handling and stability of distributed drive electric vehicles (DDEVs) under four-wheel steering (4WS) conditions, this study proposes a novel hierarchical control strategy based on a phase plane analysis. This approach involves a meticulous comparison of the stable region in the phase plane to thoroughly analyze the intricate influence of the front wheel angle, rear wheel angle, road adhesion coefficient, and longitudinal speed on the complex dynamic performances of DDEVs and to accurately determine the critical stable-state parameter. Subsequently, a hierarchical control strategy is presented as an integrated solution to achieve the coordinated control of maneuverability and stability. On the upper control level, a model predictive control (MPC) motion controller is developed, wherein the real-time adjustment of the control weight matrix is ingeniously achieved by incorporating the crucial vehicle stable-state parameter. The lower control level is responsible for the optimal torque allocation among the four wheel motors to minimize the tire load rate, thereby ensuring a sufficient tire grip margin. The optimal torque distribution for the four wheel motors is achieved using a sophisticated two-level allocation algorithm, wherein the friction ellipse is employed as a judgement condition. Finally, this developed control strategy is thoroughly validated through co-simulation utilizing the CarSim 2019 and Simulink 2020b commercial software, demonstrating the validity of the developed control strategy. The comparative results indicate that the presented controller ensures a better tracking capability to the desired vehicle state while exhibiting improved handling stability under both the double lane shifting condition and the serpentine working condition. Full article
(This article belongs to the Section Vehicle Engineering)
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<p>Nonlinear 7 DOF model of four-wheel steering.</p> Full article ">Figure 2
<p>Wheel rotation model.</p> Full article ">Figure 3
<p>Linear 2-DOF model of four-wheel steering.</p> Full article ">Figure 4
<p>Influence of the steering angles of the front and rear wheels on the stable region (<math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>60</mn> <mtext> </mtext> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>): (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>3</mn> <mi>deg</mi> </mrow> </semantics></math>; and (<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>6</mn> <mi>deg</mi> </mrow> </semantics></math>.</p> Full article ">Figure 4 Cont.
<p>Influence of the steering angles of the front and rear wheels on the stable region (<math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>60</mn> <mtext> </mtext> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>): (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> </mrow> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>3</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>3</mn> <mi>deg</mi> </mrow> </semantics></math>; and (<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>6</mn> <mi>deg</mi> <mo>,</mo> <mtext> </mtext> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>6</mn> <mi>deg</mi> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Influence of the road adhesion coefficient on the stable region (<math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>80</mn> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>): (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>.</p> Full article ">Figure 5 Cont.
<p>Influence of the road adhesion coefficient on the stable region (<math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>80</mn> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>): (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Influence of the longitudinal vehicle speed on the stable region (<math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mi>δ</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>): (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>20</mn> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>50</mn> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>80</mn> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math>; and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>110</mn> <mrow> <mi>km</mi> <mo>/</mo> <mi mathvariant="normal">h</mi> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Boundary coefficients under different steering angles of the front and rear wheels.</p> Full article ">Figure 8
<p>Framework of hierarchical control strategy for handling stability.</p> Full article ">Figure 9
<p>Framework of MPC motion controller.</p> Full article ">Figure 10
<p>The relationship between weight matrices <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mi>β</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mi>γ</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Simulation results of motion state under double lane change conditions: (<b>a</b>) trajectory; (<b>b</b>) lateral deviation; (<b>c</b>) longitudinal speed; and (<b>d</b>) front and rear wheel angles (the solid line represents the front wheel and the dotted line represents the rear wheel).</p> Full article ">Figure 12
<p>Simulation results of torque distribution under double lane change conditions: (<b>a</b>) torque of left front wheel; (<b>b</b>) torque of right front wheel; (<b>c</b>) torque of left rear wheel; and (<b>d</b>) torque of right rear wheel.</p> Full article ">Figure 13
<p>Simulation results of stability parameters under double lane change conditions: (<b>a</b>) sideslip angle; (<b>b</b>) total of tire load rate; (<b>c</b>) yaw angle; and (<b>d</b>) yaw rate.</p> Full article ">Figure 13 Cont.
<p>Simulation results of stability parameters under double lane change conditions: (<b>a</b>) sideslip angle; (<b>b</b>) total of tire load rate; (<b>c</b>) yaw angle; and (<b>d</b>) yaw rate.</p> Full article ">Figure 14
<p>Simulation results of motion state under serpentine working conditions: (<b>a</b>) trajectory; (<b>b</b>) lateral deviation; (<b>c</b>) longitudinal speed; and (<b>d</b>) front and rear wheel angles (the solid line represents the front wheel and the dotted line represents the rear wheel).</p> Full article ">Figure 15
<p>Simulation results of torque distribution under serpentine working conditions: (<b>a</b>) torque of left front wheel; (<b>b</b>) torque of right front wheel; (<b>c</b>) torque of left rear wheel; and (<b>d</b>) torque of right rear wheel.</p> Full article ">Figure 16
<p>Simulation results of stability parameters under serpentine working conditions: (<b>a</b>) sideslip angle; (<b>b</b>) total of tire load rate; (<b>c</b>) yaw angle error; and (<b>d</b>) yaw rate.</p> Full article ">Figure 16 Cont.
<p>Simulation results of stability parameters under serpentine working conditions: (<b>a</b>) sideslip angle; (<b>b</b>) total of tire load rate; (<b>c</b>) yaw angle error; and (<b>d</b>) yaw rate.</p> Full article ">
15 pages, 3227 KiB  
Article
Determination of Energy Losses of the Crank Press Mechanism
by Jan Hlavac and Jiri Dekastello
Machines 2024, 12(7), 477; https://doi.org/10.3390/machines12070477 - 15 Jul 2024
Viewed by 1189
Abstract
This paper focuses on determining the friction energy loss in the mechanism of a mechanical crank press. After defining the crank press mechanism and how it works, we describe the energy balance of a technological operation—forming. Four distinct methodologies for calculating friction loss [...] Read more.
This paper focuses on determining the friction energy loss in the mechanism of a mechanical crank press. After defining the crank press mechanism and how it works, we describe the energy balance of a technological operation—forming. Four distinct methodologies for calculating friction loss in the mechanism are then presented, namely an empirical method, a spreadsheet calculation utilising force decomposition in a crank mechanism, an analytical calculation of the dynamic behaviour of a press, and a multibody simulation. Each additional approach expands the possibilities for approaching reality, but as the primary aim of the study is to compare the approaches, these possibilities are not exploited. Multibody simulation has proved itself to be accurate and suitable for simulating press mechanisms and investigating their dynamics. Multibody simulation is a much more powerful tool that can lead to a digital twin, which can help us to develop a less energy-demanding press. Confirmation of the multibody simulation results is the main outcome of the comparison and will be used in future work. Full article
(This article belongs to the Section Machine Design and Theory)
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<p>Diagram of the crank press mechanism.</p> Full article ">Figure 2
<p>Force decomposition of a crank mechanism.</p> Full article ">Figure 3
<p>A force–displacement curve representing closed die forging for a 25 MN crank press.</p> Full article ">Figure 4
<p>An example of numerical integration (trapezoidal rule) of a torque versus crankshaft angle plot.</p> Full article ">Figure 5
<p>Spreadsheet calculation flowchart.</p> Full article ">Figure 6
<p>The graph of torques versus crankshaft angle—Calculation in a Spreadsheet.</p> Full article ">Figure 7
<p>Free-body diagrams—force and torque decomposition of a press mechanism.</p> Full article ">Figure 8
<p>Flowchart of a analytical calculation.</p> Full article ">Figure 9
<p>The graph of torques versus crankshaft angle—Analytical Dynamic Calculation.</p> Full article ">Figure 10
<p>Description of press mechanism parts in multibody model.</p> Full article ">Figure 11
<p>Schematic description of body joints in multibody model.</p> Full article ">Figure 12
<p>The graph of torques versus the crankshaft angle for the whole work stroke.</p> Full article ">
17 pages, 21683 KiB  
Article
Brush Seal Performance with Ideal Gas Working Fluid under Static Rotor Condition
by Altyib Abdallah Mahmoud Ahmed, Meihong Liu, Yuchi Kang, Juan Wang, Aboubaker I. B. Idriss and Nguyen Thi Trung Tin
Machines 2024, 12(7), 476; https://doi.org/10.3390/machines12070476 - 15 Jul 2024
Cited by 1 | Viewed by 1065
Abstract
The study investigated how variations in pressure ratio affect the leakage flow of a brush seal for both contact and clearance structures, in which the clearance is measured as the distance between the bristles tip and the rotor surface. This investigation utilized the [...] Read more.
The study investigated how variations in pressure ratio affect the leakage flow of a brush seal for both contact and clearance structures, in which the clearance is measured as the distance between the bristles tip and the rotor surface. This investigation utilized the Reynolds-Averaged-Navier–Stokes (RANS) equations alongside a two-dimensional axisymmetric anisotropic porous medium model. To verify the model’s accuracy and dependability, the obtained results were compared with previous numerical results and experimental observations, showing a satisfactory level of agreement. The results indicate that the predominant pressure drop occurs downstream of the bristle pack with the clearance model exhibiting a higher leakage rate compared to the contact model. Leakage increases proportionally with the pressure ratio, while axial velocity gradually rises and radial velocity experiences a significant increase. In conclusion, the leakage in the brush seal contact structure is significantly lower than in the clearance structure, resulting in the best performance. Full article
(This article belongs to the Section Turbomachinery)
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<p>Schematic brush seal configuration.</p> Full article ">Figure 2
<p>Geometric specifications for clearance brush seal structure.</p> Full article ">Figure 3
<p>Geometric specifications for contact brush seal structure.</p> Full article ">Figure 4
<p>The extended upstream and downstream fluid domains.</p> Full article ">Figure 5
<p>The flow chart of calculation analysis.</p> Full article ">Figure 6
<p>Calculation results for a different number of grids.</p> Full article ">Figure 7
<p>Computational mesh for clearance brush seal structure.</p> Full article ">Figure 8
<p>Computational mesh for contact brush seal structure.</p> Full article ">Figure 9
<p>The leakage rate of brush seal numerical calculation comparison [<a href="#B19-machines-12-00476" class="html-bibr">19</a>,<a href="#B29-machines-12-00476" class="html-bibr">29</a>].</p> Full article ">Figure 10
<p>Leakage distribution at different pressure ratios.</p> Full article ">Figure 11
<p>Pressure contour at pressure ratio of 2: (<b>a</b>) contact structure, (<b>b</b>) clearance structure.</p> Full article ">Figure 12
<p>Pressure contour at pressure ratio of 6: (<b>a</b>) contact structure, (<b>b</b>) clearance structure.</p> Full article ">Figure 13
<p>Velocity contour at pressure ratio of 2: (<b>a</b>) contact structure, (<b>b</b>) clearance structure.</p> Full article ">Figure 14
<p>Velocity contour at pressure ratio of 6: (<b>a</b>) contact structure, (<b>b</b>) clearance structure.</p> Full article ">Figure 15
<p>Velocity vector for contact brush seal structure: (<b>a</b>) a pressure of 2, and (<b>b</b>) a pressure of 6.</p> Full article ">Figure 16
<p>Velocity vector for clearance brush seal structure: (<b>a</b>) a pressure of 2, and (<b>b</b>) a pressure of 6.</p> Full article ">Figure 17
<p>(<b>a</b>) Axial and (<b>b</b>) radial lines for clearance structure.</p> Full article ">Figure 18
<p>(<b>a</b>) Axial and (<b>b</b>) radial lines for contact structure.</p> Full article ">Figure 19
<p>Radial and axial velocity and pressure for clearance brush seal at pressure ratio of 6: (<b>1</b>) axial pressure distribution, (<b>2</b>) axial velocity distribution, (<b>3</b>) radial pressure distribution, and (<b>4</b>) radial velocity distribution.</p> Full article ">Figure 20
<p>Radial and axial velocity and pressure for contact brush seal at pressure ratio of 6: (<b>1</b>) axial pressure distribution, (<b>2</b>) axial velocity distribution, (<b>3</b>) radial pressure distribution, and (<b>4</b>) radial velocity distribution.</p> Full article ">
13 pages, 6678 KiB  
Article
A Study on Micro-Pit Texture Parameter Optimization and Its Tribological Properties
by Yazhou Mao, Yuxuan Zhang, Jingyang Zheng, Lilin Li, Yuchun Huang, Shaolin Shi, Linyuan Wang, Jiaming Pei and Zichen Li
Machines 2024, 12(7), 475; https://doi.org/10.3390/machines12070475 - 15 Jul 2024
Viewed by 954
Abstract
In this paper, the effect of micro-dimple textures (produced by a laser) on the tribological properties of bearings is investigated. This study offers guidelines to reduce the friction torque of the bearing pair and addresses the problem of difficult start-ups after shutdowns. Micro-pits [...] Read more.
In this paper, the effect of micro-dimple textures (produced by a laser) on the tribological properties of bearings is investigated. This study offers guidelines to reduce the friction torque of the bearing pair and addresses the problem of difficult start-ups after shutdowns. Micro-pits with different texture diameters and depths were machined on the surface of journal bearings. Then, the impact of several different texture parameters on the tribological performance of the bearing pairs was studied using an orthogonal experimental design. Subsequently, the surface morphology of the bearings before and after the friction and wear test was observed using scanning electron microscopy (SEM) and energy-dispersive spectrometry (EDS). These observations were then used to determine the type/state of friction and wear, which also improves our understanding of how texture affects the service life of bearings. The results indicate that the bearings’ micro-pit surface hardness follows an approximate parabolic spatial distribution that decreases along the micro-pit wall. Furthermore, the laser processing of surface textures was found to cause hardening in certain areas, and the chemical composition of elemental carbon and oxygen at the inner surface of processed bearings increased by 31.1% and 7.9%, respectively. Moreover, abrasive wear was identified as the primary form of wear. The textured surface’s antifriction mechanism primarily functioned to trap particles, which acted as a secondary lubrication source and altered the lubrication states by serving as a medium for supplied lubricants. The results confirm that a suitable selection of texture parameters can not only effectively reduce the friction coefficient without shortening the service life of the bearing pair but also facilitate the smooth start-up of the rotor–bearing system. Full article
(This article belongs to the Section Friction and Tribology)
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<p>Surface texture machining.</p> Full article ">Figure 2
<p>Setup to test the tribological properties.</p> Full article ">Figure 3
<p>Mean main effects plot for the three variables (area density, diameter, and depth).</p> Full article ">Figure 4
<p>Measurement location and surface hardness. (<b>a</b>) Measurement location. (<b>b</b>) Surface hardness.</p> Full article ">Figure 5
<p>Variation in the micro-pit hardness for different energies. (<b>a</b>) Variation in hardness in a-a’ direction. (<b>b</b>) Variation in hardness with depth direction.</p> Full article ">Figure 6
<p>Distribution of hardness for various repetition numbers. (<b>a</b>) Hardness under various repetition times. (<b>b</b>) Relationship between hardness and depth.</p> Full article ">Figure 7
<p>Surface composition of a journal bearing.</p> Full article ">Figure 8
<p>Friction torque variation versus time.</p> Full article ">Figure 9
<p>Variation in the wearing capacity versus load.</p> Full article ">Figure 10
<p>Variation in wearing capacity with speed.</p> Full article ">Figure 11
<p>SEM morphology of the bearing surface.</p> Full article ">Figure 12
<p>Surface EDS analysis results after the test.</p> Full article ">
17 pages, 3619 KiB  
Article
The State of Health of Electrical Connectors
by Jian Song, Abhay Shukla and Roman Probst
Machines 2024, 12(7), 474; https://doi.org/10.3390/machines12070474 - 14 Jul 2024
Cited by 1 | Viewed by 1151
Abstract
For modern machines, factories and electric and autonomous vehicles, the importance of vreliable electrical connectors cannot be overstated. With an increasing number of connectors being used in machines, factories and vehicles, ensuring their reliability is crucial for comfort and safety alike. One of [...] Read more.
For modern machines, factories and electric and autonomous vehicles, the importance of vreliable electrical connectors cannot be overstated. With an increasing number of connectors being used in machines, factories and vehicles, ensuring their reliability is crucial for comfort and safety alike. One of the key indicators of reliability is the lifetime of connectors. To evaluate the lifetime of electrical connectors, a testing method and a model for calculating their lifetime based on the test data were developed. The results from these tests were compared to failure analysis data from long-term field operations. The findings indicate that the laboratory tests can accurately reproduce the main failures observed in the field. However, such lifetime tests can be time- and labor-intensive. To address this challenge, a data-driven method is proposed that predicts the lifetime of electrical connectors using statistical analysis of electrical contact resistance data collected from short-term tests. The predictions from this method were compared to actual results obtained from long-term tests. A strong correlation was observed between the contact resistance development in short-term tests and the number of failures in later stages of testing. Thus, apart from predicting the lifetime of connectors, this method can also be applied for failure prognosis in real-time operations. Full article
(This article belongs to the Special Issue Intelligent Machinery Fault Diagnosis and Maintenance)
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<p>Schematic illustration of the effect of different failure mechanisms on the force–resistance curves.</p> Full article ">Figure 2
<p>Schematic illustration of the correlation of the stress level and failure probability.</p> Full article ">Figure 3
<p>The development of the shape parameter b over the lifetime as a so-called “bathtub curve” [<a href="#B19-machines-12-00474" class="html-bibr">19</a>], graphically renewed.</p> Full article ">Figure 4
<p>Distribution of resistance values over failure probabilities according to standard normal distribution.</p> Full article ">Figure 5
<p>Contact resistance development and connector failure.</p> Full article ">Figure 6
<p>Exemplary exponential extrapolation (dotted lines) of the <math display="inline"><semantics> <mrow> <mi>S</mi> <mfenced separators="|"> <mrow> <mi>p</mi> <mo>,</mo> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math> data for different failure probabilities.</p> Full article ">Figure 7
<p>Measured and predicted failure times plotted over their respective failure probabilities (circular and diamond pointers) with logarithmic extrapolation (blue and orange dotted curves) till 63.2% failure probability to get CLT.</p> Full article ">Figure 8
<p>Schematic representation of the temperature profile during the thermal cycling test.</p> Full article ">Figure 9
<p>Illustration of the connectors placed in the four levels of the temperature chamber.</p> Full article ">Figure 10
<p>Schematic representation of the resistance measurement to ensure a constant current during the measurement.</p> Full article ">Figure 11
<p>Development of mean resistance value, as well as upper spread and number of failed contacts, of connector types A (<b>left</b>) and B (<b>right</b>).</p> Full article ">
18 pages, 6785 KiB  
Article
Impact of Installation Deviations on the Dynamic Characteristics of the Shaft System for 1 Gigawatt Hydro-Generator Unit
by Gangyun Song, Xingxing Huang, Haijun Li, Zhengwei Wang and Dong Wang
Machines 2024, 12(7), 473; https://doi.org/10.3390/machines12070473 - 12 Jul 2024
Cited by 1 | Viewed by 756
Abstract
The shaft system, transferring the kinetic energy of water flow into electrical energy, is the most critical component in hydropower plants. Installation deviations of the shaft system for a giant hydro-generator unit can have significant impacts on its dynamic characteristics and overall performance. [...] Read more.
The shaft system, transferring the kinetic energy of water flow into electrical energy, is the most critical component in hydropower plants. Installation deviations of the shaft system for a giant hydro-generator unit can have significant impacts on its dynamic characteristics and overall performance. In this investigation, a three-dimensional geometry of the shaft system of an operating hydro-generator unit prototype with a rated power of 1 GW is established. Then, the calculation model of the shaft system is generated accordingly with tetrahedral and hexahedral elements. By applying different boundary conditions, the finite-element method is used to analyze the influences of installation deviations, including shaft radial misalignment and angular misalignment, on the dynamic characteristics of the shaft system. The calculation results reveal that the installation deviations change the natural frequencies, critical speeds, and mode shapes of the shaft system to a certain degree. The natural frequencies of the backward precession motion with installation deviations are reduced by 23% and 38% for the rated speed and the maximum runaway speed. Furthermore, for the forward precession motion, they increased by 30% and 48%, respectively. The critical speeds for the shaft system with radial and angular deviations are 3.2% and 3% larger than the critical speed of the shaft system without any mounting deviations. The radial and angular installation deviations below the maximum permissible values will not result in the structural performance degradation of the 1 GW hydro-generator shaft system. The conclusion drawn in this research can be used as a valuable reference for installing other rotating machinery. Full article
(This article belongs to the Special Issue Advancements in Machines and Power Converters for Renewable Power Generation)
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<p>The full 3D model of the shaft system of a 1 GW hydro-generator unit.</p> Full article ">Figure 2
<p>The rotordynamics analysis model of the shaft system without installation deviation.</p> Full article ">Figure 3
<p>The FE model and boundary conditions of the shaft system.</p> Full article ">Figure 4
<p>The 1st-order mode shapes of the shaft system without installation deviation. (<b>a</b>) Mode shape I; (<b>b</b>) Mode shape II.</p> Full article ">Figure 5
<p>Normalized natural frequencies of the shaft system at different speeds without installation deviations.</p> Full article ">Figure 6
<p>Frequency increase rates of the shaft system at different speeds without installation deviations.</p> Full article ">Figure 7
<p>The motion of the shaft system without installation deviations. (<b>a</b>) t = 1/9 × T; (<b>b</b>) t = 2/9 × T; (<b>c</b>) t = 3/9 × T; (<b>d</b>) t = 4/9 × T; (<b>e</b>) t = 5/9 × T; (<b>f</b>) t = 6/9 × T; (<b>g</b>) t = 7/9 × T; (<b>h</b>) t = 8/9 × T; (<b>i</b>) t = T.</p> Full article ">Figure 8
<p>Campbell diagram of the shaft system without installation deviations.</p> Full article ">Figure 9
<p>Schematic diagram of the radial installation deviation of shaft system.</p> Full article ">Figure 10
<p>Normalized natural frequencies of the shaft system at different speeds with radial installation deviation.</p> Full article ">Figure 11
<p>Frequency increase rates of the shaft system at different speeds with radial installation deviation.</p> Full article ">Figure 12
<p>Campbell diagram of the shaft system with radial installation deviation.</p> Full article ">Figure 13
<p>Schematic diagram of angular installation deviation of shaft system.</p> Full article ">Figure 14
<p>Comparison of frequency increase rates of the shaft system without and with installation deviations.</p> Full article ">Figure 15
<p>Campbell diagram of the shaft system with angular installation deviation.</p> Full article ">Figure 16
<p>Comparison of critical speeds of the shaft system without and with installation deviations.</p> Full article ">
14 pages, 2372 KiB  
Article
Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm
by Zhe Dong, Binrui Zhong, Jiahuan He and Zhao Gao
Machines 2024, 12(7), 472; https://doi.org/10.3390/machines12070472 - 12 Jul 2024
Cited by 3 | Viewed by 1344
Abstract
This paper proposes a solution for the cooperative obstacle avoidance path planning problem in dual manipulator arms using an improved Rapidly Exploring Random Tree (RRT) algorithm. The dual manipulator arms are categorized into a main arm and a secondary arm. Initially, the obstacle [...] Read more.
This paper proposes a solution for the cooperative obstacle avoidance path planning problem in dual manipulator arms using an improved Rapidly Exploring Random Tree (RRT) algorithm. The dual manipulator arms are categorized into a main arm and a secondary arm. Initially, the obstacle avoidance path for the master arm is planned in the presence of static obstacles. Subsequently, the poses of the master arm during its movement are treated as dynamic obstacles for planning the obstacle avoidance path for the slave arm. A cost function incorporating a fast convergence policy is introduced. Additionally, adaptive weights between distance cost and variation cost are innovatively integrated into the cost function, along with increased weights for each joint, enhancing the algorithm’s effectiveness and feasibility in practical scenarios. The smoothness of the planned paths is improved through the introduction of interpolation functions. The improved algorithm is numerically modeled and simulated in MATLAB. The verification results demonstrate that the improved RRT algorithm proposed in this paper is both feasible and more efficient. Full article
(This article belongs to the Special Issue Recent Progress in Multi-Robot Systems)
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<p>PUMA560 robotic arm establishes a set of coordinate systems.</p> Full article ">Figure 2
<p>Overall Idea Flowchart.</p> Full article ">Figure 3
<p>Trend of the angle of the arms in the random obstacle simulation experiment. Panels (<b>a</b>,<b>b</b>) depict the trajectories of the dual–arm in a single–obstacles experiment. Panels (<b>c</b>,<b>d</b>) display the trajectory of the dual–arm movement in a five–obstacles experiment. Panels (<b>e</b>,<b>f</b>) show the trajectories of the dual–arm in a ten–obstacles experiment. The left set of three figures de–picts the trajectories of the master arm, while the right set represents those of the slave arm. Six joints in the image correspond to six colors.</p> Full article ">Figure 4
<p>One random obstacle simulation experiment. (<b>a</b>–<b>c</b>) depict the initial state main view, in–termediate state main view, and end state main view of the dual robotic arm. (<b>d</b>–<b>f</b>) depicts the initial state top view, intermediate state top view, and end state top view of the dual robotic arm.</p> Full article ">Figure 5
<p>Five random obstacle simulation experiments. (<b>a</b>–<b>c</b>) depict the initial state main view, in–termediate state main view, and end state main view of the dual robotic arm. (<b>d</b>–<b>f</b>) depicts the initial state top view, intermediate state top view, and end state top view of the dual robotic arm.</p> Full article ">Figure 6
<p>Ten random obstacles simulation experiments. (<b>a</b>–<b>c</b>) depict the initial state main view, int–ermediate state main view, and end state main view of the dual robotic arm. (<b>d</b>–<b>f</b>) depicts the initial state top view, intermediate state top view, and end state top view of the dual robotic arm.</p> Full article ">
19 pages, 2923 KiB  
Article
Reinforcement Learning-Based Auto-Optimized Parallel Prediction for Air Conditioning Energy Consumption
by Chao Gu, Shentao Yao, Yifan Miao, Ye Tian, Yuru Liu, Zhicheng Bao, Tao Wang, Baoyu Zhang, Tao Chen and Weishan Zhang
Machines 2024, 12(7), 471; https://doi.org/10.3390/machines12070471 - 12 Jul 2024
Viewed by 1053
Abstract
Air conditioning contributes a high percentage of energy consumption over the world. The efficient prediction of energy consumption can help to reduce energy consumption. Traditionally, multidimensional air conditioning energy consumption data could only be processed sequentially for each dimension, thus resulting in inefficient [...] Read more.
Air conditioning contributes a high percentage of energy consumption over the world. The efficient prediction of energy consumption can help to reduce energy consumption. Traditionally, multidimensional air conditioning energy consumption data could only be processed sequentially for each dimension, thus resulting in inefficient feature extraction. Furthermore, due to reasons such as implicit correlations between hyperparameters, automatic hyperparameter optimization (HPO) approaches can not be easily achieved. In this paper, we propose an auto-optimization parallel energy consumption prediction approach based on reinforcement learning. It can parallel process multidimensional time series data and achieve the automatic optimization of model hyperparameters, thus yielding an accurate prediction of air conditioning energy consumption. Extensive experiments on real air conditioning datasets from five factories have demonstrated that the proposed approach outperforms existing prediction solutions, with an increase in average accuracy by 11.48% and an average performance improvement of 32.48%. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Maintenance)
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<p>The Architecture of RL-AOPP Framework.</p> Full article ">Figure 2
<p>The processing flow of the calculation unit in MTCN: (<b>a</b>) The convolutional kernel first extracts features from the red region, and only in subsequent iterations does it sequentially extract features from the blue and purple regions. (<b>b</b>) Within a single iteration, the kernel can extract features in parallel from red, blue, and purple regions while preserving the original temporal characteristics of the data.</p> Full article ">Figure 3
<p>PSoftplus activation function.</p> Full article ">Figure 4
<p>Visualization of prediction results for different models.</p> Full article ">Figure 5
<p>Model convergence graph under different activation functions.</p> Full article ">Figure 6
<p>Training comparison graph of two frameworks in Hong Kong dataset.</p> Full article ">Figure 7
<p>Training comparison graph of two frameworks in Shenzhen dataset.</p> Full article ">Figure 8
<p>Different control parameters comparison of DSLD model.</p> Full article ">
11 pages, 9905 KiB  
Article
Material Extrusion 3D Printing of Micro-Porous Copper-Based Structure for Water Filters
by Nikola Kotorčević, Strahinja Milenković, Fatima Živić, Branka Jordović, Dragan Adamović, Petar Todorović and Nenad Grujović
Machines 2024, 12(7), 470; https://doi.org/10.3390/machines12070470 - 12 Jul 2024
Viewed by 929
Abstract
This paper presents 3D-printed micro-porous structures made of a Cu/PLA composite by using material extrusion 3D printing technology. A metallic filament made of 80% copper and 20% polylactic acid (PLA) was used for the 3D printing of the porous samples. We varied printing [...] Read more.
This paper presents 3D-printed micro-porous structures made of a Cu/PLA composite by using material extrusion 3D printing technology. A metallic filament made of 80% copper and 20% polylactic acid (PLA) was used for the 3D printing of the porous samples. We varied printing parameters, aiming to obtain a micro-range porosity that can serve as a water-filtering structure. The produced samples were analyzed from the aspects of dimensional accuracy, level of porosity, and capacity for water flow. Several samples were fabricated, and the water flow was exhibited for the samples with an approximate 100 µm size of the interconnected open porosity. The application of material extrusion 3D printing, as a cost-effective, widely available technology for producing micro-range porous structures, is still challenging, especially for interconnected predefined porosity with metal-based filaments. Our research showed that the optimization of 3D printing parameters can enable the fabrication of copper-based micro-porous structures, but further research is still needed. Full article
(This article belongs to the Special Issue Recent Advances in 3D Printing in Industry 4.0)
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<p>Comparison of sample 1 and sample 5 from aspect of pore sizes: (<b>a</b>) sample 1 and (<b>b</b>) sample 5.</p> Full article ">Figure 2
<p>SEM images of the test samples: sample 1 (<b>left image</b>) and sample 5 (<b>right image</b>).</p> Full article ">Figure 3
<p>Samples for water flow tests: (<b>a</b>) with open porosity (sample 1) and (<b>b</b>) sample fabricated without porosity.</p> Full article ">Figure 4
<p>Custom setup for water flow tests: (<b>a</b>) water container, test samples, syringe, PTFE tape; (<b>b</b>) suspended water flow with the sample with a non-porous structure (<a href="#machines-12-00470-f003" class="html-fig">Figure 3</a>b); (<b>c</b>) water flow through a filter with a porous structure (<a href="#machines-12-00470-f003" class="html-fig">Figure 3</a>a).</p> Full article ">Figure 5
<p>Load change during compressive water flow test with non-porous sample (<a href="#machines-12-00470-f003" class="html-fig">Figure 3</a>b).</p> Full article ">Figure 6
<p>Load change during compressive water-flow test with porous samples 1–8 (<a href="#machines-12-00470-t001" class="html-table">Table 1</a>).</p> Full article ">
16 pages, 2383 KiB  
Article
Novel Intelligent Traffic Light Controller Design
by Firas Zahwa, Chi-Tsun Cheng and Milan Simic
Machines 2024, 12(7), 469; https://doi.org/10.3390/machines12070469 - 11 Jul 2024
Viewed by 2019
Abstract
Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We examined the capacity of roads [...] Read more.
Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We examined the capacity of roads connecting intersections, considering factors such as road vehicle capacity, vehicle speed, and traffic flow volume, through detailed mathematical modeling and analysis. Control is determined by the maximum capacity of each road segment, providing valuable insights into traffic flow dynamics. Building upon this capacity and flow analysis, the research introduces a novel intelligent traffic light controller (ITLC) system based on fuzzy logic principles. By incorporating real-time traffic data and leveraging fuzzy logic algorithms, our ITLC system dynamically adjusts traffic light timings to optimize vehicle flow at two intersections. The paper discusses the design and implementation of the ITLC system, highlighting its adaptive capabilities in response to changing traffic conditions. Simulation results demonstrate the effectiveness of the ITLC system in improving traffic flow and reducing congestion at intersections. Furthermore, this research provides an analysis of the mathematical models used to calculate road capacity, offering insights into the underlying principles of traffic flow optimization. Through the simulation, we have validated the accuracy and reliability of our controller. Full article
(This article belongs to the Section Vehicle Engineering)
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<p>An illustration of 2 interconnected traffic light intersections during a FIS-Sumo controlled simulation. The arrows indicate the directions of the lanes. The yellow triangles represent vehicles, with the orange dots at their fronts indicating their turning signals and the red dots at their rears indicating take brake lights. The red and green bars at the intersections represent the state of the traffic lights.</p> Full article ">Figure 2
<p>Illustration of two adjacent intersections controlled by traffic lights controls “C” and “V.” The intersections are positioned alongside a main road, with synchronised traffic lights to ensure smooth traffic flow. The arrows indicate the directions of the lanes. The yellow lines represent all the possible routes for simulated vehicles.</p> Full article ">Figure 3
<p>Memberships functions representing the number of vehicles in the red/green lane. The input variable “no_vehicle_primary_road.” The variable is divided into three fuzzy sets: “low” (blue), “moderate” (orange), and “high” (yellow), indicating the number of vehicles on the primary road.</p> Full article ">Figure 4
<p>An illustration of fuzzy memberships functions for the input variable “waiting_time_red_lane.” The variable is divided into three fuzzy sets: “negligible” (blue), “medium” (orange), and “a lot” (yellow), representing the waiting time of vehicles in the red lane.</p> Full article ">Figure 5
<p>An illustration of fuzzy membership functions for the output variable “traffic_light_signal.” The variable is divided into two fuzzy sets: “no change” (orange) and “change” (blue), indicating whether the traffic light should remain the same or switch.</p> Full article ">Figure 6
<p>Flow chart illustrating the control process for the fuzzy logic-based traffic control simulation. The process begins with initialization and runs through a loop of 1000 steps. The system collects data on vehicles in green and red lanes, defines waiting times, and uses a fuzzy inference system (FIS) to decide whether to toggle the traffic lights. The simulation aims to optimize traffic flow by adjusting signals based on real-time conditions.</p> Full article ">Figure 7
<p>Comparison of average vehicle waiting times at red lights between the SUMO fixed-time traffic controller and the fuzzy logic traffic controller. The fuzzy logic controller significantly reduces waiting times compared to the fixed-time controller, demonstrating its effectiveness in dynamically adjusting traffic signals based on real-time traffic conditions.</p> Full article ">Figure 8
<p>Average vehicle waiting time over 1000 time steps for fuzzy logic-controlled traffic (blue line) and fixed time-controlled traffic (orange line). The fuzzy logic controller shows consistently lower and more stable waiting times compared to the fixed-time controller, which exhibits frequent spikes indicating higher delays. This highlights the superior performance of the fuzzy logic system in adapting to changing traffic conditions and reducing congestion.</p> Full article ">
21 pages, 10992 KiB  
Article
Detection of Demagnetization Faults in Electric Motors by Analyzing Inverter Based Current Data Using Machine Learning Techniques
by Daniel Walch, Christoph Blechinger, Martin Schellenberger, Maximilian Hofmann, Bernd Eckardt and Vincent R.H. Lorentz
Machines 2024, 12(7), 468; https://doi.org/10.3390/machines12070468 - 11 Jul 2024
Viewed by 1040
Abstract
Demagnetization of the rotor magnets is a significant failure mode that can occur in permanent magnet synchronous machines (PMSMs). Early detection of demagnetization faults can help change system parameters to reduce power output or ensure safety. In this paper, the effects of demagnetization [...] Read more.
Demagnetization of the rotor magnets is a significant failure mode that can occur in permanent magnet synchronous machines (PMSMs). Early detection of demagnetization faults can help change system parameters to reduce power output or ensure safety. In this paper, the effects of demagnetization faults were analyzed both in simulation and experiments using the example of drone motors. An approach was investigated to detect even minor demagnetization faults that does not require any additional sensing effort. Machine learning (ML) techniques are used to analyze the phase current data directly received from the inverter to enable anomaly detection. For this purpose, the phase current is transformed by the Fast Fourier Transform (FFT), the spectral data is then reduced in dimensionality, followed by an anomaly detection algorithm using a one-class support vector machine (OC-SVM). To ensure simplified initialization of the ML model without the need for training sets of damaged drives, only data from magnetically undamaged motors was used to train the models for anomaly detection. Different selections of considered harmonics and different metrics were investigated using the experimental data, achieving a precision of up to 99%, a specificity of up to 98%, and an accuracy of up to 90%. Full article
(This article belongs to the Section Electrical Machines and Drives)
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<p>Typical shape of magnetization curve of neodymium magnetic material for two different temperatures (green <span class="html-italic">T</span><sub>0</sub> &lt; blue <span class="html-italic">T</span><sub>1</sub>) and operating characteristics of an electric machine (gray) and recoil line (red) because of irreversible demagnetization.</p> Full article ">Figure 2
<p>Finite-element-method (FEM) model with healthy magnets (dark green) and (<b>a</b>) two magnets completely demagnetized (red) and (<b>b</b>) two magnets moderately demagnetized (blue).</p> Full article ">Figure 3
<p>Simulated FFT of the induced voltage of undamaged, complete, and moderately demagnetized magnets.</p> Full article ">Figure 4
<p>(<b>a</b>) Result of the demagnetization simulation with visible demagnetization of magnetic edges when <span class="html-italic">I<sub>d</sub></span> = 25 A and <span class="html-italic">T</span><sub>mag</sub> = 80 °C; (<b>b</b>) Modeling the demagnetized edges in FEM with area of <span class="html-italic">B</span><sub>r</sub> = 0 (pink color) and <span class="html-italic">B</span><sub>r</sub> = 100% <span class="html-italic">B</span><sub>r0</sub> (dark green color).</p> Full article ">Figure 5
<p>Simulated FFT of the induced voltage of undamaged, complete demagnetization of some magnets and all-moderate demagnetized magnets.</p> Full article ">Figure 6
<p>Simulated THD of the induced voltage at no-load for different degrees of partial (CD-SM and MD-SM) and uniform (MD-AM) demagnetization.</p> Full article ">Figure 7
<p>Laboratory setup of the performed experiments.</p> Full article ">Figure 8
<p>Exemplary section of the recorded phase currents.</p> Full article ">Figure 9
<p>Section of one investigated drone motor.</p> Full article ">Figure 10
<p>Detailed process of the model training, including hyperparameter optimization with data from damage-free motors.</p> Full article ">Figure 11
<p>Testing the pre-trained models with previously unseen data from healthy and damaged motors.</p> Full article ">Figure 12
<p>Investigated frequency spectra and their restrictions.</p> Full article ">Figure 13
<p>Averaged and normalized FFT spectrum of the measured data with marked demagnetization frequencies.</p> Full article ">Figure 14
<p>FFT spectrum limited to demagnetization harmonics.</p> Full article ">Figure 15
<p>FFT spectrum limited to motor-related frequencies up to the 16th harmonic.</p> Full article ">Figure 16
<p>Dependencies between the three main PCs of the kPCA for the different approaches: (<b>a</b>) entire FFT spectrum; (<b>b</b>) all demagnetization-related frequencies; (<b>c</b>) up to the 11th demagnetization harmonic; (<b>d</b>) up to the 7th demagnetization harmonic; (<b>e</b>) up to the 5th demagnetization harmonic; (<b>f</b>) all motor harmonics.</p> Full article ">Figure 16 Cont.
<p>Dependencies between the three main PCs of the kPCA for the different approaches: (<b>a</b>) entire FFT spectrum; (<b>b</b>) all demagnetization-related frequencies; (<b>c</b>) up to the 11th demagnetization harmonic; (<b>d</b>) up to the 7th demagnetization harmonic; (<b>e</b>) up to the 5th demagnetization harmonic; (<b>f</b>) all motor harmonics.</p> Full article ">Figure 17
<p>Confusion matrices for the different approaches: entire FFT spectrum (<b>a</b>), demagnetization harmonics (<b>b</b>), up to the 11th demagnetization harmonic (<b>c</b>), up to the 7th demagnetization harmonic (<b>d</b>), up to the 5th demagnetization harmonic (<b>e</b>), motor harmonics (<b>f</b>).</p> Full article ">
13 pages, 722 KiB  
Article
Efficiency Analysis of Die Attach Machines Using Overall Equipment Effectiveness Metrics and Failure Mode and Effects Analysis with an Ishikawa Diagram
by Rex Revian A. Guste, Klint Allen A. Mariñas and Ardvin Kester S. Ong
Machines 2024, 12(7), 467; https://doi.org/10.3390/machines12070467 - 11 Jul 2024
Viewed by 1585
Abstract
The semiconductor manufacturing sector has contributed to the advancement of technical development in the sphere of industrial applications, but one crucial factor that cannot be overlooked is the evaluation of a machine’s state. Despite the presence of advanced equipment, data on their performances [...] Read more.
The semiconductor manufacturing sector has contributed to the advancement of technical development in the sphere of industrial applications, but one crucial factor that cannot be overlooked is the evaluation of a machine’s state. Despite the presence of advanced equipment, data on their performances are not properly reviewed, resulting in a variety of concerns such as high rejection rates, lower production output, manufacturing overhead cost issues, and customer complaints. This study’s goal is to evaluate the performance of die attach machines made by a prominent subcontractor semiconductor manufacturing business in the Philippines; our findings will provide other organizations with important insights into the appropriate diagnosis of productivity difficulties via productivity metrics analyses. The study focuses on a specific type of die attach machine, with machine 10 showing to be the most troublesome, with an overall equipment effectiveness (OEE) rating of 43.57%. The Failure Mode and Effects Analysis (FMEA) identified that the primary reasons for the issue were idling, small stoppages, and breakdown loss resulting from loosened screws in the work holder. The risk priority number (RPN) was calculated to be 392, with a severity level of 7, an occurrence level of 7, and a detection level of 8. The findings provide new insight into the methods that should be included in the production process to boost efficiency and better suit the expectations of customers in a highly competitive market. Full article
(This article belongs to the Special Issue Advances in Machinery Condition Monitoring, Diagnosis and Prognosis)
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<p>Theoretical framework.</p> Full article ">Figure 2
<p>Ishikawa diagram of idling and minor stoppage errors.</p> Full article ">
19 pages, 5103 KiB  
Article
Failure Inducement Factor Analysis and Optimal Design Method of Ball Bearing Cage for Aviation Motor
by Yongcun Cui, Linshen Cai, Jingjing Wang and Xiaoguo Gao
Machines 2024, 12(7), 466; https://doi.org/10.3390/machines12070466 - 10 Jul 2024
Viewed by 677
Abstract
In experiments of aviation motor bearings, when the deep-groove ball bearings are subjected to an overturning moment at high speed, it often happens that the rivet on the cage breaks and the debris invades the raceway, resulting in bearing failure. To address the [...] Read more.
In experiments of aviation motor bearings, when the deep-groove ball bearings are subjected to an overturning moment at high speed, it often happens that the rivet on the cage breaks and the debris invades the raceway, resulting in bearing failure. To address the problem of early failure of deep-groove ball bearing cages and rivets in aviation motors, the causes of early failure were analyzed from the aspect of cage design in this study. The influence of the raceway and cage structure parameters on the dynamic contact characteristics of the rolling element and cage under the action of overturning torque were analyzed, the weak link of the cage was determined, and the cage design parameters were optimized. The results show that with an increase in the cage width and pocket radius, the impact force between the ball and cage first decreases and then increases, and the tilt angle of the cage gradually decreases. A larger channel radius and smaller clearance can slow down the interaction between the cage and the rolling element and make the cage run more smoothly. Increasing the thickness of the cage can ensure that the rivet part of the cage is at a low stress level, and the risk of premature fatigue failure at the rivet part can be reduced by maintaining a small gap–fit relationship between the rivet and rivet holes. The research results indicate that the working condition adaptability of the bearing cage for aviation motors can be improved, and the design method for this type of bearing can be enhanced. Full article
(This article belongs to the Section Electrical Machines and Drives)
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<p>Coordinate systems in a bearing.</p> Full article ">Figure 2
<p>Force between the ball and the inner and outer raceways.</p> Full article ">Figure 3
<p>Force acting between the ball and the cage’s pockets.</p> Full article ">Figure 4
<p>Stress concentration diagram of cage connection part. (<b>a</b>) Result of modified Norbert method. (<b>b</b>) Result of finite element calculation.</p> Full article ">Figure 5
<p>Relationship between pocket radius and cage dynamic contact characteristics. (<b>a</b>) Relationship between impact force of steel ball and cage and radius of pockets. (<b>b</b>) Relationship between maximum rivet stress and pocket radius. (<b>c</b>) Relationship between cage dynamic characteristics and radius of pockets. (<b>d</b>) Relationship between cage stability and radius of pockets.</p> Full article ">Figure 6
<p>Relationship between cage width and contact characteristics between ball and cage. (<b>a</b>) Relationship between impact force of the steel ball and cage and width of the cage. (<b>b</b>) Relationship between maximum stress at rivet position and cage width. (<b>c</b>) Relationship between cage dynamic characteristics and cage width.</p> Full article ">Figure 7
<p>Relationship between thickness and contact characteristics between ball and cage. (<b>a</b>) Relationship between impact force and maximum stress of rivet and cage thickness. (<b>b</b>) Relationship between the maximum rivet stress and cage thickness.</p> Full article ">Figure 8
<p>Relationship between cage structure parameters and impact force.</p> Full article ">Figure 9
<p>Relationship between channel structure parameters and cage dynamic contact characteristics. (<b>a</b>) Relationship between impact force and coefficient of curvature radius of inner raceway. (<b>b</b>) Relationship between slipping rate, cage tilt angle, and inner raceway curvature radius coefficient. (<b>c</b>) Relationship between impact force and curvature radius coefficient of outer raceway. (<b>d</b>) Relationship between slipping rate, cage tilt angle of cage, and curvature radius coefficient of outer raceway. (<b>e</b>) Relationship between impact force and radial clearance between steel ball and cage. (<b>f</b>) Relationship between slipping rate, cage tilt angle, and cage radial clearance.</p> Full article ">Figure 9 Cont.
<p>Relationship between channel structure parameters and cage dynamic contact characteristics. (<b>a</b>) Relationship between impact force and coefficient of curvature radius of inner raceway. (<b>b</b>) Relationship between slipping rate, cage tilt angle, and inner raceway curvature radius coefficient. (<b>c</b>) Relationship between impact force and curvature radius coefficient of outer raceway. (<b>d</b>) Relationship between slipping rate, cage tilt angle of cage, and curvature radius coefficient of outer raceway. (<b>e</b>) Relationship between impact force and radial clearance between steel ball and cage. (<b>f</b>) Relationship between slipping rate, cage tilt angle, and cage radial clearance.</p> Full article ">Figure 10
<p>Effect of the fit relation on stress distribution at the rivet position. (<b>a</b>) Stress distribution in the rivet in interference-fit with the cage. (<b>b</b>) Stress distribution in the rivet in gap-fit with cage clearance. (<b>c</b>) Relationship between the fit clearance and maximum stress in the rivet.</p> Full article ">Figure 11
<p>Test machine and each experimental component diagram. (<b>a</b>) Photograph of testing machine. (<b>b</b>) Schematic of cage speed measurement. (<b>c</b>) Testing machine size diagram.</p> Full article ">Figure 12
<p>Photographs of tested bearings. (<b>a</b>) Original bearing test result. (<b>b</b>) Experimental results of bearings with improved cage parameters.</p> Full article ">
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