Search Results (1,139)

This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users’ outdoor activity duration preferences and emotional experiences. OMS, as a key element in modern urban design, significantly enhances residents’ quality of life and promotes public health. Accurately understanding and predicting users’ emotional needs is the core challenge in optimizing OMS. In this study, the Kansei Engineering (KE) framework is applied, using fuzzy clustering to reduce the dimensionality of emotional descriptors, while RST is employed for attribute reduction to select five key design features that influence users’ emotions. Subsequently, the PSO-SVR model is applied to establish the nonlinear mapping relationship between these design features and users’ emotions, predicting the optimal configuration of OMS design. The results indicate that the optimized OMS design significantly enhances users’ intention to stay in the space, as reflected by higher ratings for emotional descriptors and increased preferences for longer outdoor activity duration, all exceeding the median score of the scale. Additionally, comparative analysis shows that the PSO-SVR model outperforms traditional methods (e.g., BPNN, RF, and SVR) in terms of accuracy and generalization for predictions. These findings demonstrate that the proposed method effectively improves the emotional performance of OMS design and offers a solid optimization framework along with practical guidance for future urban public space design. The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. This method not only offers a new theoretical perspective for OMS design but also establishes a scientific framework to accurately incorporate users’ emotional needs into the design process. The method contributes new knowledge to the field of urban design, promotes public health and well-being, and provides a solid foundation for future applications in different urban environments. Full article

The body structures and motion stability of worm-like and snake-like robots have garnered significant research interest. Recently, innovative serial–parallel hybrid segmented robots have emerged as a fundamental platform for a wide range of motion modes. To address the hyper-redundancy characteristics of these hybrid structures, we propose a novel caterpillar-inspired Stable Segment Update (SSU) gait generation approach, establishing a unified framework for multi-segment robot gait generation. Drawing inspiration from the locomotion of natural caterpillars, the segments are modeled as rigid bodies with six degrees of freedom (DOF). The SSU gait generation method is specifically designed to parameterize caterpillar-like gaits. An inverse kinematics solution is derived by analyzing the forward kinematics and identifying the minimum lifting segment, framing the problem as a single-segment end-effector tracking task. Three distinct parameter sets are introduced within the SSU method to account for the stability of robot motion. These parameters, represented as discrete hump waves, are intended to improve motion efficiency during locomotion. Furthermore, the trajectories for each swinging segment are determined through kinematic analysis. Experimental results validate the effectiveness of the proposed SSU multi-gait generation method, demonstrating the successful traversal of gaps and rough terrain. Full article

This article examines the effect of the number of inserts in a milling head on cutting forces during machining and the resulting surface roughness. An experimental study was used to compare results using different insert configurations while maintaining a constant feed per tooth. The resulting cutting forces and surface roughness were analyzed and discussed in the context of the optimal setting of cutting conditions. It was found that a reduced number of inserts does not necessarily lead to a reduction in cutting forces during machining and that while maintaining the feed per tooth with a reduced number of inserts, the roughness is not significantly affected. An unexpected result was that inserts can differ in terms of the surface quality achieved. This research also shows that individual inserts can vary substantially in the force load they generate, a phenomenon that can be attributed to their dimensional differences. This study provides valuable insights for industrial applications that require precision machining concerning cutting forces and surface quality. It can potentially improve the efficiency and quality of machining in industrial applications. Full article

In today’s tech world of digitalization, engineers are leveraging tools such as artificial intelligence for analyzing data in order to enhance their capability in evaluating product quality effectively. This research study adds value by applying algorithms and various machine learning techniques—such as support vector regression, Gaussian process regression, and artificial neural networks—on a dataset related to the grinding process of UNS S34700 steel. What sets this study apart is its consideration of factors like three types of grinding wheels, four distinct cooling solutions, and seven varied depths of cut. These parameters are assessed for their impact on surface roughness and grinding forces, resulting in the conversion of information into insights. A relational equation with 25 coefficients is developed, using optimized algorithms to predict surface roughness with an 85 percent accuracy and grinding forces with a 90 percent accuracy rate. Learning from machine models like the Gaussian process regression exhibited stability, with an R² value of 0.98 and a mean accuracy of 93 percent. Artificial neural networks achieved an R² value of 0.96, and an accuracy rate of 90 percent. These findings suggest that machine learning techniques are versatile and precise when dealing with datasets. They align well with digitalization and predictive trends. In conclusion; machine learning provides flexibility and superior accuracy for predicting data trends compared to the formulaic approach, which is contained to existing datasets only. The versatility of machine learning highlights its significance in engineering practices for making data-informed decisions. Full article

This study aims to optimize the Wire Electrical Discharge Machining (EDM) process parameters for aluminum 6061 alloy reinforced with Mg and MoS₂ using the Box–Behnken (BBD) design and the non-dominated sorting genetic (NSGA-II) algorithm. The objective is to enhance the machining efficiency and quality of the composite material. The Box–Behnken (BBD) design was utilized to design a set of experiments with varying levels of process parameters, comprising pulse-on time, servo volt, and current. The material removal rate and surface roughness were considered as machining responses for optimization. These responses were measured and used to develop a mathematical model. The NSGA-II, a multi-objective optimization algorithm, was then applied to search for the optimal combination of process parameters that simultaneously maximizes the material removal rate and minimizes the electrode wear rate and surface roughness. The algorithm generated and evolved a set of Pareto-optimal solutions, providing a trade-off between conflicting objectives. The results of the optimization process were analyzed to identify the optimal process parameters that lead to improved machining performance. The study revealed optimal Wire Electrical Discharge Machining (WEDM) parameters for Al6061/Mg/MoS₂ composites using NSGA-II. The optimized parameters, including a pulse-on time (Ton) of 105 µs, servo voltage (SV) of 35 V, and peak current (PC) of 31 A, resulted in a Material Removal Rate (MRR) of 7.51 mm³/min and a surface roughness (SR) of 1.97 µm. This represents a 15% improvement in the MRR and a 20% reduction in the SR compared to non-optimized settings, demonstrating the efficiency of the BBD-NSGA-II approach. Full article

The aim of this study was to compare the mechanical and physicochemical properties of Ti6Al4V alloy samples produced using 3D printing (Direct Metal Laser Sintering) and bar after plastic working. Both sets of samples were subjected to various surface-processing methods, including sandblasting, heat treatment (hardening for 120 min at 820 ± 10 °C, followed by cooling to room temperature), mechanical polishing, and steam sterilization. This research included macroscopic surface evaluation before and after pitting corrosion resistance tests, metallographic microscopic research, scanning electron microscopy, and energy-dispersive spectroscopy, as well as measurements of hardness, roughness, and surface wettability. The results showed that heat and surface treatment (grinding and mechanical polishing) significantly increased the material’s hardness and corrosion resistance. Furthermore, the steam sterilization process had a positive effect by increasing surface wettability, which is important for biomedical applications, as higher wettability promotes better integration with biological tissues. This is especially relevant in implantology, where surface properties influence osseointegration and overall biocompatibility. In summary, these findings indicate that the selection of manufacturing method and the application of subsequent treatment processes significantly affect the mechanical and physicochemical properties of Ti6Al4V alloy, thereby influencing its performance and suitability for diverse engineering and biomedical applications. Full article

Image recognition models often struggle with class imbalance, which can impede their performance. To overcome this issue, researchers have extensively used resampling methods, traditionally focused on tabular datasets. In contrast to the original method, which generates data at the data level, this paper introduces a novel strategy that combines contrastive learning with the Synthetic Minority Oversampling Technique based on Rough Set Theory (SMOTE-RSB) specifically tailored for imbalanced image datasets. Our method leverages contrastive learning to refine representation learning and balance features, thus effectively mitigating the challenges of imbalanced image classification. We begin by extracting features using a pre-trained contrastive learning encoder. Subsequently, SMOTE-RSB is applied to these features to augment underrepresented classes and reduce irrelevant features. We evaluated our approach on several modified benchmark datasets, including CIFAR-10, SVHN, and ImageNet-LT, achieving notable improvements: an F1 score of 72.43% and a Gmean of 82.53% on the CIFAR-10 long-tailed dataset, F1 scores up to 79.57% and Gmean of 88.20% on various SVHN datasets, and a Top-1 accuracy of 68.67% on ImageNet-LT. Both qualitative and quantitative results confirm the effectiveness of our method in managing imbalances in image datasets. Additional ablation studies exploring various contrastive learning models and oversampling techniques highlight the flexibility and efficiency of our approach across different settings, underscoring its significant potential for enhancing imbalanced image classification. Full article

The decision tree algorithm is widely used in various classification problems due to its ease of implementation and strong interpretability. However, information in the real world often has uncertainty and partial reliability, which poses challenges for classification tasks. To address this issue, this paper proposes a fuzzy decision tree based on fuzzy rough sets and Z-numbers, aimed at enhancing the decision tree’s ability to handle fuzzy and uncertain information. In the aspect of rule extraction, we combine the fuzzy rough set model to propose a fuzzy confidence based on lower approximation as a metric for attribute selection, effectively addressing the role of imprecise knowledge in classification. In terms of the tree structure, the concept of Z-numbers is introduced, specifically focusing on the fuzzy constraint reliability B, making the information representation more aligned with human evaluation habits, as well as using Z-number rules to replace traditional fuzzy rules in constructing the fuzzy decision tree. Furthermore, as generating Z-numbers still presents certain challenges, this paper also establishes a method for reasonably generating Z-numbers in situations with limited information, utilizing the generated fuzzy constraint reliability B to adjust fuzzy numbers A. Finally, the proposed decision tree algorithm is experimentally compared with other classifiers, and the results indicate that this algorithm demonstrates higher classification accuracy and a more concise tree structure when handling datasets containing fuzzy and uncertain factors. This research enriches the existing research on fuzzy decision trees and shows greater potential in solving practical problems. Full article

Modeling carbonate growth in fractures and pores is important for understanding carbon sequestration in the environment or when supersaturated solutions are injected into rocks. Here, we study the simple but nontrivial problem of calcite growth on fractures with rough walls of the same mineral using kinetic Monte Carlo simulations of attachment and detachment of molecules and scaling approaches. First, we consider wedge-shaped fracture walls whose upper terraces are in the same low-energy planes and show that the valleys are slowly filled by the propagation of parallel monolayer steps in the wedge sides. The growth ceases when the walls reach these low-energy configurations so that a gap between the walls may not be filled. Second, we consider fracture walls with equally separated monolayer steps (vicinal surfaces with roughness below 1 nm) and show that growth by step propagation will eventually clog the fracture gap. In both cases, scaling approaches predict the times to attain the final configurations as a function of the initial geometry and the step-propagation velocity, which is set by the saturation index. The same reasoning applied to a random wall geometry shows that step propagation leads to lateral filling of surface valleys until the wall reaches the low-energy crystalline plane that has the smallest initial density of molecules. Thus, the final configurations of the fracture walls are much more sensitive to the crystallography than to the roughness or the local curvature. The framework developed here may be used to determine those configurations, the times to reach them, and the mass of deposited mineral. Effects of transport limitations are discussed when the fracture gap is significantly narrowed. Full article

Studying the factors affecting machining accuracy, surface quality, and machining efficiency in the powerful honing machining process system, analyzing the basic law between various errors and machining quality, exploring the method of evaluating the quality of honing, and improving the machining quality and transmission performance of hardened gears has important engineering application value. Firstly, this paper establishes an effective abrasive grains micro-edge honing quality evaluation model, proposes a method based on the Trapezoidal Fuzzy Analytic Hierarchy Process (Tra-FAHP) and Set Pair Analysis (SPA) to comprehensively evaluate the quality of the honing process, and obtains the influence weights of each factor on the quality of honing. Secondly, the paper analyzes the influence rules of three types of abrasive grain sizes on helix error, tooth pitch error, tooth profile error, surface roughness, and honing efficiency. Finally, the correctness of the established comprehensive evaluation model of honing quality was verified with the threshold method and weights. The research results show that the model can correctly evaluate the quality of hardened gear honing and can be applied to studying the influence of abrasive grain micro-edge honing on machining characteristics. Full article

This study examines the influence of drift angle on the wave and flow field generated by a submarine navigating through a density-stratified fluid. Employing a numerical methodology, this research computed the viscous flow field around the SUBOFF bare hull under conditions of oblique shipping maneuvers. The analytical framework relies on the Reynolds-Averaged Navier–Stokes (RANS) equations, supplemented by the Re-Normalization Group (RNG) k-ε turbulence model and the Volume of Fluid (VOF) method. The initial phases of this study involved verifying grid convergence and the accuracy of the numerical methods used. Subsequently, numerical simulations were performed across a spectrum of drift angles while maintaining a fixed Froude number of F_n = 0.5, with submergence depths set at 1.1 D and 2.0 D. The analysis focused on the wave profiles at both the free surface and the internal surface. The results indicate that the presence of a drift angle produces significant alterations in the characteristics of the free surface and internal surface when compared with straight-ahead motion. Specifically, the asymmetry in the flow field is enhanced, and the variability in the roughness of the free surface is pronounced. Full article

This paper presents exploratory findings suggesting that mathematics teachers can implement Rough Draft Math (RDM) by making small, incremental changes that align with their current practices and local contexts, including curriculum materials, with minimal support. Following a conference presentation and/or reading a book about pedagogy, teachers reported shifts in their thinking that facilitated their interest in enacting RDM and small changes they made to their teaching. The flexibility of RDM, as a general concept rather than a set of prescribed practices, allowed teachers to incorporate RDM to meet their own teaching goals. We propose that this adaptability enables teachers to incorporate RDM into their classrooms incrementally, reflecting their existing objectives for their students. Full article

This paper explores the effect of bonding size on the shear performance of ultra-high-performance concrete (UHPC) and normal concrete (NC). The study includes two sets of direct shear tests on a total of 16 Z-shaped UHPC-NC bonded specimens. The first set consists of eight direct shear tests on the chiseled UHPC-NC interface with an average roughness of 4 mm (referred to as series C), from the authors’ previous study. The second set involves eight direct shear tests on the chiseled UHPC-NC interface with additional short shear steel rebars (referred to as series CS) that possess identical roughness to the first set of tests. The study discusses the failure modes, shear stress–slip behavior, and strain histories of the UHPC-NC interfaces with varying bonding sizes and shear mechanisms. A finite element model incorporating the cohesive zone model for the UHPC-NC interface was developed to gain insights into the shear bond evolutions. Our experimental results show that the two sets of direct shear specimens exhibit similar size effects in the shear stiffness, bonding strength, and interfacial slippage of the UHPC-NC interface. The use of shear steel rebars mitigated the impact of interfacial size on the bond shear behavior, thereby enhancing shear stiffness and reducing susceptibility to brittle damage. Numerical simulations indicate that the shear stress inhomogeneity coefficients for the CS specimens with bonding heights of 100 mm, 200 mm, 330 mm, and 440 mm were 1.2%, 1.8%, 11.9%, and 17.4%, respectively. The findings of this study provide valuable insights for optimizing UHPC applications in the repair and strengthening of concrete structures. Full article

With a view to improve measurements, this paper presents a statistical approach for characterizing the behaviour of roughness parameters based on measurements performed on ground surface topographies (grit #080/#120). A S neox^TM (Sensofar^®, Terrassa, Spain), equipped with three optical instrument modes (Focus Variation (FV), Coherence Scanning Interferometry (CSI), and Confocal Microscopy (CM)), is used according to a specific measurement plan, called Morphomeca Monitoring, including topography representativeness and several time-based measurements. Previously applied to the Sa parameter, the statistical approach based here solely on the Quality Index (QI) has now been extended to a multi-parameter approach. Firstly, the study focuses on detecting and explaining parameter disturbances in raw data by identifying and quantifying outliers of the parameter’s values, as a new first indicator. This allows us to draw parallels between these outliers and the surface topography, providing reflection tracks. Secondly, the statistical approach is applied to highlight disturbed parameters concerning the instrument mode used and the concerned grit level with two other indicators computed from QI, named homogeneity and number of modes. The applied method shows that a cleaning of the data containing the parameters values is necessary to remove outlier values, and a set of roughness parameters could be determined according to the assessment of the indicators. The final aim is to provide a set of parameters which best describe the measurement conditions based on monitoring data, statistical indexes, and surface topographies. It is shown that the parameters Sal, Sz and Sci are the most reliable roughness parameters, unlike Sdq and S5p, which appear as the most unstable parameters. More globally, the volume roughness parameters appear as the most stable, differing from the form parameters. This investigated point of view offers thus a complementary framework for improving measurement processes. In addition, this method aims to provide a global and more generalizable alternative than traditional methods of uncertainty calculation, based on a thorough analysis of multi-parameter and statistical indexes. Full article

This study analyses a set of phenomena occurring in the burnished surface layer at the initial moment of deformation formation. The aim of the present research was to explain the phenomena occurring in the top layer of the material during burnishing. The presented analyses include selected laboratory and experimental studies of the process involved in forming burnished surface layers. As shown, conducting an analysis of these processes is purposeful and important because the processes affecting final deformations determine the definitive properties of the burnished surface layers. The final results should help to increase the durability and smoothness of the surface of the products obtained. The feasibility of applying computer technology to determine the three-dimensional shape of the deformation zone formation based on measurements of the stereometry of the contact zone of the burnishing tool with the workpiece material is presented. The process of forming a deformation zone was analysed, revealing that irregularities left over from prior treatment are permanently deformed, and a new structure of irregularities is formed on the machined surface, conditioned by the mechanical, geometric, and kinematic factors of the process. Crucial to this are qualities such as the burnishing load (pressure), the type, shape, and dimensions of the tool, the properties of the workpiece material, and the roughness of the surface before burnishing. The analyses presented here include the first stage of processing, in which initial contact is made with the workpiece, and the period of actual processing, during which plastic deformation of the material occurs in three perpendicular directions, leading to the formation of a material wave on the machined surface just in front of the burnishing tool. Full article