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Generative Artificial Intelligence and Machine Learning in Industrial Processes and...
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Generative Artificial Intelligence and Machine Learning in Industrial Processes and Manufacturing

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 15 December 2024 | Viewed by 2446

Special Issue Editor

Dr. Ananda Maiti

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Waurn Ponds 3216, Australia
Interests: Industrial Internet of Things; algorithms; web programming; instrumentation; data mining; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Generative Artificial Intelligence (AI) and Machine learning have revolutionized various industries by enabling the automation, optimization, and predictive analytics of processes. In recent years, these technologies have been increasingly applied in industrial settings to enhance efficiency and decision-making operations. This Special Issue aims to explore the latest advancements, challenges, and opportunities associated with machine learning and generative AI in industrial applications. Industry 5.0 is expected to create new paradigms of human–AI collaboration and enhance productivity and safety when performing complex tasks.

The scope of this Special Issue includes, but is not limited to, the following:

  • Machine learning algorithms for predictive maintenance in manufacturing
  • Generative AI for design optimization in engineering
  • Deep learning for quality control in production processes
  • AI-driven decision support systems for industrial operations
  • Applications of machine learning in energy management and sustainability
  • Applications such as digital twins and supply chain management
  • Case studies and real-world implementations of machine learning in industrial settings

We invite researchers, practitioners, and experts to submit their original research papers, reviews, and surveys to this Special Issue. Extended conference papers are also welcome, but they should comprise at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. Submissions will undergo a rigorous peer-review process to ensure their high quality and relevance to the theme of the Special Issue.

Dr. Ananda Maiti
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. Computers 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 1800 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

  • generative AI
  • supply chain
  • digital twins
  • construction
  • cloud-based manufacturing
  • prototyping
  • regulatory technologies
  • Industry 5.0

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

31 pages, 13095 KiB  
Article
Self-Adaptive Evolutionary Info Variational Autoencoder
by Toby A. Emm and Yu Zhang
Computers 2024, 13(8), 214; https://doi.org/10.3390/computers13080214 - 22 Aug 2024
Viewed by 919
Abstract
With the advent of increasingly powerful machine learning algorithms and the ability to rapidly obtain accurate aerodynamic performance data, there has been a steady rise in the use of algorithms for automated aerodynamic design optimisation. However, long training times, high-dimensional design spaces and [...] Read more.
With the advent of increasingly powerful machine learning algorithms and the ability to rapidly obtain accurate aerodynamic performance data, there has been a steady rise in the use of algorithms for automated aerodynamic design optimisation. However, long training times, high-dimensional design spaces and rapid geometry alteration pose barriers to this becoming an efficient and worthwhile process. The variational autoencoder (VAE) is a probabilistic generative model capable of learning a low-dimensional representation of high-dimensional input data. Despite their impressive power, VAEs suffer from several issues, resulting in poor model performance and limiting optimisation capability. Several approaches have been proposed in attempts to fix these issues. This study combines the approaches of loss function modification with evolutionary hyperparameter tuning, introducing a new self-adaptive evolutionary info variational autoencoder (SA-eInfoVAE). The proposed model is validated against previous models on the MNIST handwritten digits dataset, assessing the total model performance. The proposed model is then applied to an aircraft image dataset to assess the applicability and complications involved with complex datasets such as those used for aerodynamic design optimisation. The results obtained on the MNIST dataset show improved inference in conjunction with increased generative and reconstructive performance. This is validated through a thorough comparison against baseline models, including quantitative metrics reconstruction error, loss function calculation and disentanglement percentage. A number of qualitative image plots provide further comparison of the generative and reconstructive performance, as well as the strength of latent encodings. Furthermore, the results on the aircraft image dataset show the proposed model can produce high-quality reconstructions and latent encodings. The analysis suggests, given a high-quality dataset and optimal network structure, the proposed model is capable of outperforming the current VAE models, reducing the training time cost and improving the quality of automated aerodynamic design optimisation. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence and Machine Learning in Industrial Processes and Manufacturing)
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14 pages, 5149 KiB  
Article
Implementation of Integrated Development Environment for Machine Vision-Based IEC 61131-3
by Sun Lim, Un-Hyeong Ham and Seong-Min Han
Computers 2024, 13(7), 172; https://doi.org/10.3390/computers13070172 - 15 Jul 2024
Viewed by 1125
Abstract
IEC 61131-3 is an international standard for developing standardized software for automation and control systems. Machine vision systems are a prominent technology in the field of computer vision and are widely used in various industries, such as manufacturing, robotics, healthcare, and automotive, and [...] Read more.
IEC 61131-3 is an international standard for developing standardized software for automation and control systems. Machine vision systems are a prominent technology in the field of computer vision and are widely used in various industries, such as manufacturing, robotics, healthcare, and automotive, and are often combined with AI technologies. In industrial automation systems, software developed for defect detection or product classification typically involves separate systems for automation and machine vision programs, leading to increased system complexity and unnecessary resource wastage. To address these limitations, this study proposes an IEC 61131-3-based integrated development environment for programmable machine vision. We selected 11 APIs commonly used in machine vision systems, evaluated their functions in an IEC 61131-3 compliant development environment, and measured the performance of representative machine vision applications. This approach demonstrates the feasibility of developing PLC and machine vision programs within a single-controller system. We investigated the impact of controller performance on function execution. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence and Machine Learning in Industrial Processes and Manufacturing)
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