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Processes
Special Issues
Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0
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Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 12058

Special Issue Editors

Dr. Xiaosong Du

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Interests: artificial intelligence; machine learning; scientific machine learning; multidisciplinary design optimization; aircraft design; electric vertical takeoff and landing drones
Special Issues, Collections and Topics in MDPI journals
Dr. Devina P. Sanjaya

E-Mail Website
Guest Editor
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA
Interests: computational fluid dynamics; machine learning; optimization

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) makes the core of the Industry 4.0 revolution. AI, especially subset machine learning (ML), has been advancing the mechanical engineering area. In particular, ML could help fine-tune product quality and optimize operations during the manufacturing process for improving product quality and reducing time to market. In addition, ML-based predictive failure enables optimal maintenance time, which saves cost and time. Furthermore, optimal control incorporated with reinforcement learning plays a key role in scheduling in production, supply chain, and Industry 4.0 systems. In the meantime, stakeholders achieve optimal product management through novel optimization architectures enabled by ML surrogate modeling. In summary, ML, optimal control, and optimization together have been pushing forward the leading edge in manufacturing and Industry 4.0.

This Special Issue on “Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0” targets original and novel research products on ML, control, and optimization, with application emphasis on practical mechanical engineering problems.

Topics include, but are not limited to:

  1. Novel ML algorithm development demonstrated on mechanical engineering problems (including manufacturing, aerospace engineering, etc.).
  2. State-of-the-art ML methods introduced for large-scale optimal control or practical mechanical engineering applications.
  3. Challenging analysis or design under uncertainty for mechanical engineering problems through ML methods.

Dr. Xiaosong Du
Dr. Devina P. Sanjaya
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. Processes is an international peer-reviewed open access monthly 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

  • machine learning
  • mechanical engineering
  • engineering design optimization
  • optimal control
  • aerospace engineering
  • design under uncertainty
  • reinforcement learning
  • surrogate modeling
  • manufacturing

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Published Papers (6 papers)

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Research

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21 pages, 5000 KiB  
Article
Surrogate-Based Multidisciplinary Optimization for the Takeoff Trajectory Design of Electric Drones
by Samuel Sisk and Xiaosong Du
Processes 2024, 12(9), 1864; https://doi.org/10.3390/pr12091864 - 31 Aug 2024
Viewed by 683
Abstract
Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which [...] Read more.
Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which prohibit long-distance tasks and broader applications. Since the takeoff process of eVTOL aircraft demands excessive energy and couples multiple subsystems (such as aerodynamics and propulsion), multidisciplinary analysis and optimization (MDAO) become essential. Conventional MDAO, however, iteratively evaluates high-fidelity simulation models, making the whole process computationally intensive. Surrogates, in lieu of simulation models, empower efficient MDAO with the premise of sufficient accuracy, but naive surrogate modeling could result in an enormous training cost. Thus, this work develops a twin-generator generative adversarial network (twinGAN) model to intelligently parameterize takeoff power and wing angle profiles of an eVTOL aircraft. The twinGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus A3 Vahana aircraft. The twinGAN provisioned two-fold dimensionality reductions. First, twinGAN generated only realistic trajectory profiles of power and wing angle, which implicitly reduced the design space. Second, twinGAN with three variables represented the takeoff trajectory profiles originally parameterized using 40 B-spline control points, which explicitly reduced the design space while maintaining sufficient variability, as verified by fitting optimization. Moreover, surrogate modeling with respect to the three twinGAN variables, total takeoff time, mass, and power efficiency, reached around 99% accuracy for all the quantities of interest (such as vertical displacement). Surrogate-based, derivative-free optimizations obtained over 95% accuracy and reduced the required computational time by around 26 times compared with simulation-based, gradient-based optimization. Thus, the novelty of this work lies in the fact that the twinGAN model intelligently parameterized trajectory designs, which achieved implicit and explicit dimensionality reductions. Additionally, twinGAN-enabled surrogate modeling enabled the efficient takeoff trajectory design with high accuracy and computational cost reduction. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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16 pages, 3200 KiB  
Article
Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning
by Alexios Papacharalampopoulos, Kosmas Alexopoulos, Paolo Catti, Panagiotis Stavropoulos and George Chryssolouris
Processes 2024, 12(6), 1262; https://doi.org/10.3390/pr12061262 - 19 Jun 2024
Cited by 1 | Viewed by 943
Abstract
Monitoring tool wear is key for the optimization of manufacturing processes. To achieve this, machine learning (ML) has provided mechanisms that work adequately on setups that measure the cutting force of a tool through the use of force sensors. However, given the increased [...] Read more.
Monitoring tool wear is key for the optimization of manufacturing processes. To achieve this, machine learning (ML) has provided mechanisms that work adequately on setups that measure the cutting force of a tool through the use of force sensors. However, given the increased focus on sustainability, i.e., in the context of reducing complexity, time and energy consumption required to train ML algorithms on large datasets dictate the use of smaller samples for training. Herein, the concepts of active learning (AL) and transfer learning (TL) are simultaneously studied concerning their ability to meet the aforementioned objective. A method is presented which utilizes AL for training ML models with less data and then it utilizes TL to further reduce the need for training data when ML models are transferred from one industrial case to another. The method is tested and verified upon an industrially relevant scenario to estimate the tool wear during the turning process of two manufacturing companies. The results indicated that through the application of the AL and TL methodologies, in both companies, it was possible to achieve high accuracy during the training of the final model (1 and 0.93 for manufacturing companies B and A, respectively). Additionally, reproducibility of the results has been tested to strengthen the outcomes of this study, resulting in a small standard deviation of 0.031 in the performance metrics used to evaluate the models. Thus, the novelty presented in this paper is the presentation of a straightforward approach to apply AL and TL in the context of tool wear classification to reduce the dependency on large amounts of high-quality data. The results show that the synergetic combination of AL with TL can reduce the need for data required for training ML models for tool wear prediction. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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16 pages, 2543 KiB  
Article
Novel Triplet Loss-Based Domain Generalization Network for Bearing Fault Diagnosis with Unseen Load Condition
by Bingbing Shen, Min Zhang, Le Yao and Zhihuan Song
Processes 2024, 12(5), 882; https://doi.org/10.3390/pr12050882 - 26 Apr 2024
Viewed by 1161
Abstract
In the real industrial manufacturing process, due to the constantly changing operational loads of equipment, it is difficult to collect data from all load conditions as the source domain signal for fault diagnosis. Therefore, the appearance of unseen load vibration signals in the [...] Read more.
In the real industrial manufacturing process, due to the constantly changing operational loads of equipment, it is difficult to collect data from all load conditions as the source domain signal for fault diagnosis. Therefore, the appearance of unseen load vibration signals in the target domain presents a challenge and research hotspot in fault diagnosis. This paper proposes a triplet loss-based domain generalization network (TL-DGN) and then applies it to an unseen domain bearing fault diagnosis. TL-DGN first utilizes a feature extractor to construct a multi-source domain classification loss. Furthermore, it measures the distance between class data from different domains using triplet loss. The introduced triplet loss can narrow the distance between samples of the same class in the feature space and widen the distance between samples of different classes based on the action of the cross-entropy loss function. It can reduce the dependency of the classification boundary on bearing operational loads, resulting in a more generalized classification model. Finally, two comparative experiments with fault diagnosis models without triplet loss and other classification models demonstrate that the proposed model achieves superior fault diagnosis performance. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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24 pages, 10759 KiB  
Article
Optimization of Smart Textiles Robotic Arm Path Planning: A Model-Free Deep Reinforcement Learning Approach with Inverse Kinematics
by Di Zhao, Zhenyu Ding, Wenjie Li, Sen Zhao and Yuhong Du
Processes 2024, 12(1), 156; https://doi.org/10.3390/pr12010156 - 9 Jan 2024
Viewed by 1838
Abstract
In the era of Industry 4.0, optimizing the trajectory of intelligent textile robotic arms within cluttered configuration spaces for enhanced operational safety and efficiency has emerged as a pivotal area of research. Traditional path-planning methodologies predominantly employ inverse kinematics. However, the inherent non-uniqueness [...] Read more.
In the era of Industry 4.0, optimizing the trajectory of intelligent textile robotic arms within cluttered configuration spaces for enhanced operational safety and efficiency has emerged as a pivotal area of research. Traditional path-planning methodologies predominantly employ inverse kinematics. However, the inherent non-uniqueness of these solutions often leads to varied motion patterns in identical settings, potentially leading to convergence issues and hazardous collisions. A further complication arises from an overemphasis on the tool center point, which can cause algorithms to settle into suboptimal solutions. To address these intricacies, our study introduces an innovative path-planning optimization strategy utilizing a model-free, deep reinforcement learning framework guided by inverse kinematics experience. We developed a deep reinforcement learning algorithm for path planning, amalgamating environmental enhancement strategies with multi-information entropy-based geometric optimization. This approach specifically targets the challenges outlined. Extensive experimental analyses affirm the enhanced optimality and robustness of our method in robotic arm path planning, especially when integrated with inverse kinematics, outperforming existing algorithms in terms of safety. This advancement notably elevates the operational efficiency and safety of intelligent textile robotic arms, offering a groundbreaking and pragmatic solution for path planning in real-world intelligent knitting applications. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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17 pages, 4166 KiB  
Article
Wear Prediction of Tool Based on Modal Decomposition and MCNN-BiLSTM
by Zengpeng He, Yefeng Liu, Xinfu Pang and Qichun Zhang
Processes 2023, 11(10), 2988; https://doi.org/10.3390/pr11102988 - 16 Oct 2023
Cited by 2 | Viewed by 1332
Abstract
Metal cutting is a complex process with strong randomness and nonlinear characteristics in its dynamic behavior, while tool wear or fractures will have an immediate impact on the product surface quality and machining precision. A combined prediction method comprising modal decomposition, multi-channel input, [...] Read more.
Metal cutting is a complex process with strong randomness and nonlinear characteristics in its dynamic behavior, while tool wear or fractures will have an immediate impact on the product surface quality and machining precision. A combined prediction method comprising modal decomposition, multi-channel input, a multi-scale Convolutional neural network (CNN), and a bidirectional long-short term memory network (BiLSTM) is presented to monitor tool condition and to predict tool-wear value in real time. This method considers both digital signal features and prediction network model problems. First, we perform correlation analysis on the gathered sensor signals using Pearson and Spearman techniques to efficiently reduce the amount of input signals. Second, we use Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to enhance the local characteristics of the signal, then boost the neural network’s identification accuracy. In addition, the deconstructed signal is converted into a multi-channel input matrix, from which multi-scale spatial characteristics and two-way temporal features are recovered using multi-scale CNN and BiLSTM, respectively. Finally, this strategy is adopted in simulation verification using real PHM data. The wear prediction experimental results show that, in the developed model, C1, C4, and C6 have good prediction performance, with RMSE of 8.2968, 12.8521, 7.6667, and MAE of 6.7914, 9.9263, and 5.9884, respectively, significantly lower than SVR, B-BiLSTM, and 2DCNN models. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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Review

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28 pages, 3147 KiB  
Review
Framework for the Strategic Adoption of Industry 4.0: A Focus on Intelligent Systems
by Joel Serey, Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Rodrigo Ternero, Claudia Duran, Jorge Sabattin and Sebastian Gutierrez
Processes 2023, 11(10), 2973; https://doi.org/10.3390/pr11102973 - 13 Oct 2023
Cited by 4 | Viewed by 4817
Abstract
Despite growing interest in smart manufacturing, there is little information on how organizations can approach the alignment of strategic processes with Industry 4.0. This study seeks to fill this knowledge gap by developing a framework for the integration of Industry 4.0 techniques and [...] Read more.
Despite growing interest in smart manufacturing, there is little information on how organizations can approach the alignment of strategic processes with Industry 4.0. This study seeks to fill this knowledge gap by developing a framework for the integration of Industry 4.0 techniques and artificial intelligence systems. This framework will serve as a conceptual guide in the digital transformation processes toward Industry 4.0. This study involved a systematic literature review of the important methodological proposals and identification of thematic axes, research topics, strategic objectives, challenges, drivers, technological trends, models, and design architectures. In total, 160 articles were selected (120 were published between 2017 and 2022). The results provide insights into the prospects for strategic alignment in the adoption of Industry 4.0. The conceptualization of the framework shows that Industry 4.0 needs strategic adjustments mainly in seven objectives (business model, change mindset, skills, human resources, service level, ecosystem, interconnection, and absorption capacity) derived from 10 thematic axes and 28 research topics. Understanding the strategic adoption of Industry 4.0 and artificial intelligence is vital for industrial organizations to stay competitive and relevant in a constantly evolving business landscape. Full article
(This article belongs to the Special Issue Machine Learning, Control, and Optimization in Manufacturing and Industry 4.0)
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