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Mathematics
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Recent Advances in Machine Learning Methods for Mechanical Engineering
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Recent Advances in Machine Learning Methods for Mechanical Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3708

Special Issue Editors

Prof. Dr. Marko Nagode

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, SI-1000 Ljubljana, Slovenia
Interests: thermo-mechanical fatigue; fatigue life prediction; creep life prediction; finite element analysis; cyclic plasticity; reliability; finite mixture modelling; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Branislav Panić

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva Ulica 6, 1000 Ljubljana, Slovenia
Interests: reliability; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Recent advances in traditional machine learning and deep learning methods are becoming increasingly popular in technology. They have been successfully applied in many fields, such as design, manufacturing, diagnostics, computational mechanics, robotics, etc. With the further increase in computational power and the amount of experimental data collected, as well as the increasing power of these methods, we expect this trend to increase in the future. However, there is a lack of systematic reviews and topic collections dealing with their application in mechanical engineering. For this reason, we are opening a Special Issue with scientific contributions on current problems in mechanical engineering, the solution to which is the use of machine learning methods.

The scientific contributions expected here should relate to current problems in mechanical engineering, the solution of which can be achieved through the creative use of existing machine learning methods and their modifications—for example, the use of machine learning methods in design optimization, topology optimization, etc., improvements of existing machine learning and deep learning methods in diagnostics and reliability of complex machines, or the use or improvement of machine learning and deep learning methods for constitutive modelling of materials. The above examples are just illustrative, and the contributions are not only limited to these areas. We are open to your contributions, and you are cordially invited to submit your manuscripts.

Prof. Dr. Marko Nagode
Dr. Branislav Panić
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. Mathematics 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 2600 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

  • design
  • manufacturing
  • constitutive modeling of materials
  • system reliability
  • structural reliability
  • condition monitoring
  • structural health monitoring
  • computational mechanics
  • robotics
  • design of experiments
  • Industry 4.0
  • deep learning
  • machine learning
  • artificial intelligence

Published Papers (4 papers)

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Research

24 pages, 7372 KiB  
Article
Infinite-Horizon Degradation Control Based on Optimization of Degradation-Aware Cost Function
by Amirhossein Hosseinzadeh Dadash and Niclas Björsell
Mathematics 2024, 12(5), 729; https://doi.org/10.3390/math12050729 - 29 Feb 2024
Viewed by 485
Abstract
Controlling machine degradation enhances the accuracy of the remaining-useful-life estimation and offers the ability to control failure type and time. In order to achieve optimal degradation control, the system controller must be cognizant of the consequences of its actions by considering the degradation [...] Read more.
Controlling machine degradation enhances the accuracy of the remaining-useful-life estimation and offers the ability to control failure type and time. In order to achieve optimal degradation control, the system controller must be cognizant of the consequences of its actions by considering the degradation each action imposes on the system. This article presents a method for designing cost-aware controllers for linear systems, to increase system reliability and availability through degradation control. The proposed framework enables learning independent of the system’s physical structure and working conditions, enabling controllers to choose actions that reduce system degradation while increasing system lifetime. To this end, the cost of each controller’s action is calculated based on its effect on the state of health. A mathematical structure is proposed, to incorporate these costs into the cost function of the linear–quadratic controller, allowing for optimal feedback for degradation control. A simulation validates the proposed method, demonstrating that the optimal-control method based on the proposed cost function outperforms the linear–quadratic regulator in several ways. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Methods for Mechanical Engineering)
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22 pages, 1042 KiB  
Article
Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering
by Branislav Panić, Marko Nagode, Jernej Klemenc and Simon Oman
Mathematics 2023, 11(23), 4800; https://doi.org/10.3390/math11234800 - 28 Nov 2023
Cited by 1 | Viewed by 790
Abstract
The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need [...] Read more.
The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be able to extract the desired information from images. In such cases, we are usually gently pushed towards the best possible clustering method, as it is often more robust than simple traditional image processing methods. We investigate the usefulness of combining two clustering methods for unsupervised image segmentation. We use the mixture models to extract the color and spatial image features based on the obtained output segments. Then we construct a similarity matrix (adjacency matrix) based on these features to perform spectral clustering. In between, we propose a label noise correction using Markov random fields. We investigate the usefulness of our method on many hand-crafted images of different objects with different shapes, colorization, and noise. Compared to other clustering methods, our proposal performs better, with 10% higher accuracy. Compared to state-of-the-art supervised image segmentation methods based on deep convolutional neural networks, our proposal proves to be competitive. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Methods for Mechanical Engineering)
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25 pages, 2046 KiB  
Article
MoMo: Mouse-Based Motion Planning for Optimized Grasping to Declutter Objects Using a Mobile Robotic Manipulator
by Senthil Kumar Jagatheesaperumal, Varun Prakash Rajamohan, Abdul Khader Jilani Saudagar, Abdullah AlTameem, Muhammad Sajjad and Khan Muhammad
Mathematics 2023, 11(20), 4371; https://doi.org/10.3390/math11204371 - 21 Oct 2023
Viewed by 1033
Abstract
The aim of this study is to develop a cost-effective and efficient mobile robotic manipulator designed for decluttering objects in both domestic and industrial settings. To accomplish this objective, we implemented a deep learning approach utilizing YOLO for accurate object detection. In addition, [...] Read more.
The aim of this study is to develop a cost-effective and efficient mobile robotic manipulator designed for decluttering objects in both domestic and industrial settings. To accomplish this objective, we implemented a deep learning approach utilizing YOLO for accurate object detection. In addition, we incorporated inverse kinematics to facilitate the precise positioning, placing, and movement of the robotic arms toward the desired object location. To enhance the robot’s navigational capabilities within the environment, we devised an innovative algorithm named “MoMo”, which effectively utilizes odometry data. Through careful integration of these algorithms, our goal is to optimize grasp planning for object decluttering while simultaneously reducing the computational burden and associated costs of such systems. During the experimentation phase, the developed mobile robotic manipulator, following the MoMo path planning strategy, exhibited an impressive average path length coverage of 421.04 cm after completing 10 navigation trials. This performance surpassed that of other state-of-the-art path planning algorithms in reaching the target. Additionally, the MoMo strategy demonstrated superior efficiency, achieving an average coverage time of just 16.84 s, outperforming alternative methods. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Methods for Mechanical Engineering)
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22 pages, 21318 KiB  
Article
Intelligent Fault Diagnosis of Robotic Strain Wave Gear Reducer Using Area-Metric-Based Sampling
by Yeong Rim Noh, Salman Khalid, Heung Soo Kim and Seung-Kyum Choi
Mathematics 2023, 11(19), 4081; https://doi.org/10.3390/math11194081 - 26 Sep 2023
Cited by 1 | Viewed by 992
Abstract
The main challenge with rotating machine fault diagnosis is the condition monitoring of machines undergoing nonstationary operations. One possible way of efficiently handling this situation is to use the deep learning (DL) method. However, most DL methods have difficulties when the issue of [...] Read more.
The main challenge with rotating machine fault diagnosis is the condition monitoring of machines undergoing nonstationary operations. One possible way of efficiently handling this situation is to use the deep learning (DL) method. However, most DL methods have difficulties when the issue of imbalanced datasets occurs. This paper proposes a novel framework to mitigate this issue by developing an area-metric-based sampling method. In the proposed process, the new sampling scheme can identify which locations of the datasets can potentially have a high degree of surprise. The basic idea of the proposed method is whenever significant deviations from the area metrics are observed to populate more sample points. In addition, to improve the training accuracy of the DL method, the obtained sampled datasets are transformed into a continuous wavelet transform (CWT)-based scalogram representing the time–frequency component. The dilated convolutional neural network (CNN) is also introduced as a classification process with the altered images. The efficacy of the proposed method is demonstrated with fault diagnosis problems for welding robots. The obtained results are also compared with existing methods. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Methods for Mechanical Engineering)
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