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Applied Sciences
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Machine Learning in Manufacturing Technology and Systems
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Machine Learning in Manufacturing Technology and Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 17575

Special Issue Editor

Prof. Dr. Doriana Marilena D’Addona

E-Mail Website
Guest Editor
Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: machining processes and automation; intelligent computation for manufacturing technology and systems; cognitive sensor applications for process monitoring; reconfigurable and flexible machine tools
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern manufacturing systems are ever more developing as cyber–physical systems that are able to collect data from the surrounding physical environment and use it in order to make autonomous decisions using smart functionalities.

Such systems increasingly rely on the employment of heterogeneous sensors to collect data from the manufacturing process, product, and system which can be utilized for different purposes such as the condition monitoring of machines, processes and tools; predictive maintenance; quality control; resource management; etc. To effectively take advantage of these sensors in view of realizing the smart factories of the future, artificial intelligence techniques such as machine learning can be applied.

AI methods are taken to include the development and implementation of paradigms that exhibit characteristics associated with intelligence in human behavior. Machine learning is a subset of AI, representing one of the ways to achieve AI through systems that can learn by themselves from experience and get smarter and smarter over time without human intervention.

This special issue particularly welcomes papers from all over the world on the topic of machine learning paradigms in manufacturing systems.

Prof. Dr. Doriana Marilena D’Addona
Guest Editor

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

  • manufacturing
  • machine learning
  • intelligent manufacturing systems
  • smart manufacturing
  • cognitive systems
  • cognitive process and condition monitoring

Published Papers (5 papers)

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Research

14 pages, 4400 KiB  
Article
Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer
by Domenico Buongiorno, Donato Caramia, Luca Di Ruscio, Nicola Longo, Simone Panicucci, Giovanni Di Stefano, Vitoantonio Bevilacqua and Antonio Brunetti
Appl. Sci. 2022, 12(22), 11581; https://doi.org/10.3390/app122211581 - 15 Nov 2022
Cited by 2 | Viewed by 1915
Abstract
In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping [...] Read more.
In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network’s weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0%. Full article
(This article belongs to the Special Issue Machine Learning in Manufacturing Technology and Systems)
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13 pages, 1307 KiB  
Article
Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
by Luigi Nele, Giulio Mattera and Mario Vozza
Appl. Sci. 2022, 12(7), 3615; https://doi.org/10.3390/app12073615 - 02 Apr 2022
Cited by 10 | Viewed by 3813
Abstract
Welding is one of the most complex industrial processes because it is challenging to model, control, and inspect. In particular, the quality inspection process is critical because it is a complex and time-consuming activity. This research aims to propose a system of online [...] Read more.
Welding is one of the most complex industrial processes because it is challenging to model, control, and inspect. In particular, the quality inspection process is critical because it is a complex and time-consuming activity. This research aims to propose a system of online inspection of the quality of the welded items with gas metal arc welding (GMAW) technology through the use of neural networks to speed up the inspection process. In particular, following experimental tests, the deviations of the welding parameters—such as current, voltage, and welding speed—from the Welding Procedure Specification was used to train a fully connected deep neural network, once labels have been obtained for each weld seam of a multi-pass welding procedure through non-destructive testing, which made it possible to find a correspondence between welding defects (e.g., porosity, lack of penetrations, etc.) and process parameters. The final results have shown an accuracy greater than 93% in defects classification and an inference time of less than 150 ms, which allow us to use this method for real-time purposes. Furthermore in this work networks were trained to reach a smaller false positive rate for the classification task on test data, to reduce the presence of faulty parts among non-defective parts. Full article
(This article belongs to the Special Issue Machine Learning in Manufacturing Technology and Systems)
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17 pages, 9009 KiB  
Article
Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps
by Jaegyeong Cha and Jongpil Jeong
Appl. Sci. 2022, 12(4), 2209; https://doi.org/10.3390/app12042209 - 20 Feb 2022
Cited by 13 | Viewed by 3239
Abstract
Detecting defect patterns in semiconductors is very important for discovering the fundamental causes of production defects. In particular, because mixed defects have become more likely with the development of technology, finding them has become more complex than can be performed by conventional wafer [...] Read more.
Detecting defect patterns in semiconductors is very important for discovering the fundamental causes of production defects. In particular, because mixed defects have become more likely with the development of technology, finding them has become more complex than can be performed by conventional wafer defect detection. In this paper, we propose an improved U-Net model using a residual attention block that combines an attention mechanism with a residual block to segment a mixed defect. By using the proposed method, we can extract an improved feature map by suppressing irrelevant features and paying attention to the defect to be found. Experimental results show that the proposed model outperforms those in the existing studies. Full article
(This article belongs to the Special Issue Machine Learning in Manufacturing Technology and Systems)
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19 pages, 19337 KiB  
Article
Evaluation of Vision-Based Hand Tool Tracking Methods for Quality Assessment and Training in Human-Centered Industry 4.0
by Irio De Feudis, Domenico Buongiorno, Stefano Grossi, Gianluca Losito, Antonio Brunetti, Nicola Longo, Giovanni Di Stefano and Vitoantonio Bevilacqua
Appl. Sci. 2022, 12(4), 1796; https://doi.org/10.3390/app12041796 - 09 Feb 2022
Cited by 7 | Viewed by 3216
Abstract
Smart industrial workstations for the training and evaluation of workers are an innovative approach to face the problems of manufacturing quality assessment and fast training. However, such products do not implement algorithms that are able to accurately track the pose of a hand [...] Read more.
Smart industrial workstations for the training and evaluation of workers are an innovative approach to face the problems of manufacturing quality assessment and fast training. However, such products do not implement algorithms that are able to accurately track the pose of a hand tool that might also be partially occluded by the operator’s hands. In the best case, the already proposed systems roughly track the position of the operator’s hand center assuming that a certain task has been performed if the hand center position is close enough to a specified area. The problem of the pose estimation of 3D objects, including the hand tool, is an open and debated problem. The methods that lead to high accuracies are time consuming and require a 3D model of the object to detect, which is why they cannot be adopted for a real-time training system. The rise in deep learning has stimulated the search for better-performing vision-based solutions. Nevertheless, the problem of hand tool pose estimation for assembly and training procedures appears to not have been extensively investigated. In this study, four different vision-based methods based, respectively, on ArUco markers, OpenPose, Azure Kinect Body Tracking and the YOLO network have been proposed in order to estimate the position of a specific point of interest of the tool that has to be tracked in real-time during an assembly or maintenance procedure. The proposed approaches have been tested on a real scenario with four users handling a power drill simulating three different conditions during an assembly procedure. The performance of the methods has been evaluated and compared with the HTC Vive tracking system as a benchmark. Then, the advantages and drawbacks in terms of the accuracy and invasiveness of the method have been discussed. The authors can state that OpenPose is the most robust proposal arising from the study. The authors will investigate the OpenPose performance in more depth in further studies. The framework appears to be very interesting regarding its integration into a smart workstation for quality assessment and training. Full article
(This article belongs to the Special Issue Machine Learning in Manufacturing Technology and Systems)
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19 pages, 4690 KiB  
Article
Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
by Kai Guo, Xiang Wan, Lilan Liu, Zenggui Gao and Muchen Yang
Appl. Sci. 2021, 11(16), 7733; https://doi.org/10.3390/app11167733 - 22 Aug 2021
Cited by 37 | Viewed by 4311
Abstract
Digital twin (DT) is a key technology for realizing the interconnection and intelligent operation of the physical world and the world of information and provides a new paradigm for fault diagnosis. Traditional machine learning algorithms require a balanced dataset. Training and testing sets [...] Read more.
Digital twin (DT) is a key technology for realizing the interconnection and intelligent operation of the physical world and the world of information and provides a new paradigm for fault diagnosis. Traditional machine learning algorithms require a balanced dataset. Training and testing sets must have the same distribution. Training a good generalization model is difficult in an actual production line operation process. Fault diagnosis technology based on the digital twin uses its ultrarealistic, multisystem, and high-precision characteristics to simulate fault data that are difficult to obtain in an actual production line to train a reliable fault diagnosis model. In this article, we first propose an improved random forest (IRF) algorithm, which reselects decision trees with high accuracy and large differences through hierarchical clustering and gives them weights. Digital twin technology is used to simulate a large number of balanced datasets to train the model, and the trained model can be transferred to a physical production line through transfer learning for fault diagnosis. Finally, the feasibility of our proposed algorithm is verified through a case study of an automobile rear axle assembly line, for which the accuracy of the proposed algorithm reaches 97.8%. The traditional machine learning plus digital twin fault diagnosis method proposed in this paper involves some generalization, and thus has practical value when extended to other fields. Full article
(This article belongs to the Special Issue Machine Learning in Manufacturing Technology and Systems)
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