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Applied Sciences
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Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing Ⅱ
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Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing Ⅱ

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 3659

Special Issue Editors

Dr. Haizea González-Barrio

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Interests: super abrasive machining; milling; manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Amaia Calleja-Ochoa

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Interests: machining processes control and optimization; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 is now underway, changing traditional manufacturing processes into smart manufacturing. Smart manufacturing is one of the main industries to make full use of artificial intelligence and machine-learning technologies. Artificial intelligence is making machines smarter than before in the manufacturing industry by addressing how to build computers that improve automatically with experience. This Special Issue is open to new findings and approaches related to the current challenges and opportunities for the applications of artificial intelligence in smart manufacturing. We encourage researchers to contribute to this Special Issue, including, but not being limited to, the following subject areas:

  • Real-time monitoring with machine learning;
  • Artificial intelligence for predictive maintenance;
  • Production scheduling with reinforcement learning;
  • Artificial intelligence and robotics in smart manufacturing;
  • IoT-enabled smart manufacturing;
  • Digital twin-driven smart manufacturing.

Dr. Haizea González-Barrio
Dr. Amaia Calleja-Ochoa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart manufacturing
  • machine learning
  • digital twins monitoring and control in manufacturing
  • artificial intelligence
  • IoT-enabled smart manufacturing

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Published Papers (2 papers)

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Research

14 pages, 7118 KiB  
Article
The Influence of the Gap Phenomenon on the Occurrence of Consecutive Discharges in WEDM Through High-Speed Video Camera Observation
by Jun Wang, José Antonio Sánchez, Borja Izquierdo and Izaro Ayesta
Appl. Sci. 2024, 14(20), 9475; https://doi.org/10.3390/app14209475 - 17 Oct 2024
Viewed by 724
Abstract
The Wire Electrical Discharge Machining (WEDM) process is an accurate method for manufacturing high-added-value components for industry. Continuous developments in the process have resulted in specialized machines used in sectors such as aerospace and biomedical engineering. However, some fundamental aspects of the discharge [...] Read more.
The Wire Electrical Discharge Machining (WEDM) process is an accurate method for manufacturing high-added-value components for industry. Continuous developments in the process have resulted in specialized machines used in sectors such as aerospace and biomedical engineering. However, some fundamental aspects of the discharge process remain unresolved. This work aims to study the influence of discharge location and bubble expansion on the occurrence of subsequent discharges. A high-speed video camera observation system was constructed to capture images of each discharge. From the acquired images, an algorithm was devised to determine the discharge location based on grayscale analysis. Moreover, the voltage and current waveforms of the discharges and the framing signals of the high-speed video camera were then obtained using an oscilloscope. Synchronizing the observation images and signals allowed for calculating the delay time for each single discharge. The results indicate that most of the discharges occurred near the boundary of the bubble and during bubble expansion. This finding has been observed for a variety of machining conditions and can be explained by the effect of the debris particles concentrated at the bubble boundary. This study provides useful information for better understanding the discharge process in WEDM. Full article
(This article belongs to the Special Issue Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing Ⅱ)
►▼ Show Figures

Figure 1

Figure 1
<p>Experimental plan and procedure.</p> Full article ">Figure 2
<p>Experimental observation setup.</p> Full article ">Figure 3
<p>Schematic diagram of the experiment: (<b>a</b>) determining the initial position of the wire; (<b>b</b>) wire movement during the experiment.</p> Full article ">Figure 4
<p>Construction of the observation system.</p> Full article ">Figure 5
<p>Image of discharge obtained from experimental observation.</p> Full article ">Figure 6
<p>One cycle of bubble oscillation.</p> Full article ">Figure 7
<p>Recorded signals: gap voltage, discharge current, and framing signal.</p> Full article ">Figure 8
<p>Discharge location identified by the grayscale analysis.</p> Full article ">Figure 9
<p>Histogram of the distance between discharges: (<b>a</b>) between <span class="html-italic">d<sub>i</sub></span> and <span class="html-italic">d<sub>i</sub></span><sub>−1</sub>; (<b>b</b>) between <span class="html-italic">d<sub>i</sub></span> and <span class="html-italic">d<sub>i</sub></span><sub>−5</sub>; (<b>c</b>) between <span class="html-italic">d<sub>i</sub></span> and <span class="html-italic">d<sub>i</sub></span><sub>−20</sub>; and (<b>d</b>) between two consecutive discharges in the random model.</p> Full article ">Figure 9 Cont.
<p>Histogram of the distance between discharges: (<b>a</b>) between <span class="html-italic">d<sub>i</sub></span> and <span class="html-italic">d<sub>i</sub></span><sub>−1</sub>; (<b>b</b>) between <span class="html-italic">d<sub>i</sub></span> and <span class="html-italic">d<sub>i</sub></span><sub>−5</sub>; (<b>c</b>) between <span class="html-italic">d<sub>i</sub></span> and <span class="html-italic">d<sub>i</sub></span><sub>−20</sub>; and (<b>d</b>) between two consecutive discharges in the random model.</p> Full article ">Figure 10
<p>Size measurement of bubbles.</p> Full article ">Figure 11
<p>Distance between discharge location of <span class="html-italic">d<sub>i</sub></span> and the center of the bubble generated by <span class="html-italic">d<sub>i</sub></span><sub>−1</sub>.</p> Full article ">Figure 12
<p>Comparison of bubble radius and <span class="html-italic">l<sub>i</sub></span>.</p> Full article ">Figure 13
<p>Comparison of discharge delay time.</p> Full article ">Figure 14
<p>Relationship between the undischarged rate and discharge delay time.</p> Full article ">Figure 15
<p>Oscillation of bubbles under different gap voltage.</p> Full article ">Figure 16
<p>Relationship between the undischarged rate and discharge delay time under different peak discharge currents.</p> Full article ">
19 pages, 3303 KiB  
Article
Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
by José Silva, Pedro Coelho, Luzia Saraiva, Paulo Vaz, Pedro Martins and Alfonso López-Rivero
Appl. Sci. 2024, 14(5), 1850; https://doi.org/10.3390/app14051850 - 23 Feb 2024
Cited by 4 | Viewed by 1986
Abstract
Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this [...] Read more.
Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing Ⅱ)
►▼ Show Figures

Figure 1

Figure 1
<p>YOLO timeline.</p> Full article ">Figure 2
<p>Proposed framework for model training.</p> Full article ">Figure 3
<p>Proposed framework for real-time processing.</p> Full article ">Figure 4
<p>Prototype setup.</p> Full article ">Figure 5
<p>Captured images of the fully assembled pneumatic cylinder (complete).</p> Full article ">Figure 6
<p>Correlogram of the different instances in the dataset.</p> Full article ">Figure 7
<p>(<b>a</b>) Confusion matrix and (<b>b</b>) precision/recall curve.</p> Full article ">Figure 8
<p>Validation process of the assembly quality control: (<b>a</b>) NOT OK; (<b>b</b>) OK.</p> Full article ">
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