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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: 31 July 2025 | Viewed by 10361

Special Issue Editor

Dr. Ananda Maiti

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Waurn Ponds, VIC 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 policies can be found here.

Published Papers (5 papers)

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Research

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25 pages, 11555 KiB  
Article
Scalable Data Transformation Models for Physics-Informed Neural Networks (PINNs) in Digital Twin-Enabled Prognostics and Health Management (PHM) Applications
by Atuahene Kwasi Barimah, Ogwo Precious Onu, Octavian Niculita, Andrew Cowell and Don McGlinchey
Computers 2025, 14(4), 121; https://doi.org/10.3390/computers14040121 - 26 Mar 2025
Viewed by 605
Abstract
Digital twin (DT) technology has become a key enabler for prognostics and health management (PHM) in complex industrial systems, yet scaling predictive models for multi-component degradation (MCD) scenarios remains challenging, particularly when transferring insights from predictive models of smaller systems developed with limited [...] Read more.
Digital twin (DT) technology has become a key enabler for prognostics and health management (PHM) in complex industrial systems, yet scaling predictive models for multi-component degradation (MCD) scenarios remains challenging, particularly when transferring insights from predictive models of smaller systems developed with limited data to larger systems. To address this, a physics-informed neural network (PINN) framework that integrates a standardized scaling methodology, enabling scalable DT analytics for MCD prognostics, was developed in this paper. Our approach employs a systematic DevOps workflow that features containerized PINN DT analytics deployed on a Kubernetes cluster for dynamic resource optimization, a real-time DT platform (PTC ThingWorx™), and a custom API for bidirectional data exchange that connects the cluster to the DT platform. A key contribution of this paper is the scalable DT model, which facilitates transfer learning of degradation patterns across heterogeneous hydraulic systems. Three (3) hydraulic system configurations were modeled, analyzing multi-component filter degradation under pump speeds of 700–900 RPM. Trained on limited data from a reference system, the scaled PINN model achieved 88.98% accuracy for initial degradation detection at 900 RPM—outperforming an unscaled baseline of 64.13%—with consistent improvements across various speeds and thresholds. This work advances PHM analytics by reducing costs and development time, providing a scalable framework for cross-system DT deployment. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence and Machine Learning in Industrial Processes and Manufacturing)
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26 pages, 643 KiB  
Article
Gen-Optimizer: A Generative AI Framework for Strategic Business Cost Optimization
by Nuruzzaman Faruqui, Nidadavolu Venkat Durga Sai Siva Vara Prasad Raju, Shanmugasundaram Sivakumar, Nikhil Patel, Shinoy Vengaramkode Bhaskaran, Shapla Khanam and Touhid Bhuiyan
Computers 2025, 14(2), 59; https://doi.org/10.3390/computers14020059 - 10 Feb 2025
Cited by 1 | Viewed by 2449
Abstract
Strategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, a Generative AI-based framework designed to analyze and optimize business costs through intelligent decision support. The framework employs a transformer-based model with over [...] Read more.
Strategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, a Generative AI-based framework designed to analyze and optimize business costs through intelligent decision support. The framework employs a transformer-based model with over 140 million parameters, fine-tuned using a diverse dataset of cost-related business scenarios. By leveraging generative capabilities, Gen-Optimizer minimizes inefficiencies, automates cost analysis tasks, and provides actionable insights to decision-makers. The proposed framework achieves exceptional performance metrics, including a prediction accuracy of 93.2%, precision of 93.5%, recall of 93.1%, and an F1-score of 93.3%. The perplexity score of 20.17 demonstrates the model’s superior language understanding and generative abilities. Gen-Optimizer was tested in real-world scenarios, demonstrating its ability to reduce operational costs by 4.11% across key business functions. Furthermore, it aligns with sustainability objectives, promoting resource efficiency and reducing waste. This paper highlights the transformative potential of Generative AI in business cost management, paving the way for scalable, intelligent, and cost-effective solutions. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence and Machine Learning in Industrial Processes and Manufacturing)
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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 2003
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 1563
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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Review

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26 pages, 677 KiB  
Review
Exploring Data Analysis Methods in Generative Models: From Fine-Tuning to RAG Implementation
by Bogdan Mihai Guțu and Nirvana Popescu
Computers 2024, 13(12), 327; https://doi.org/10.3390/computers13120327 - 5 Dec 2024
Cited by 1 | Viewed by 2797
Abstract
The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for [...] Read more.
The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for the analysis of large private datasets. This article is a review of data analysis techniques based on generative models, with a focus on large language models (LLMs). It covers the strengths, limitations, and applications of methods like the fine-tuning of LLMs and retrieval-augmented generation (RAG). This study consolidates, analyzes, and interprets the findings from the literature to provide a coherent overview of the current research landscape on this topic, aiming to guide effective, privacy-conscious data analysis and exploring future improvements, especially for low-resource languages. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence and Machine Learning in Industrial Processes and Manufacturing)
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