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Electronics
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Applications of Artificial Intelligence in Intelligent Manufacturing
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Applications of Artificial Intelligence in Intelligent Manufacturing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 785

Special Issue Editor

Dr. Mohammad Shahin

E-Mail Website
Guest Editor
Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA
Interests: Lean Six Sigma; sustainability; AI; Industry 5.0

Special Issue Information

Dear Colleagues,

As a cornerstone of industrial advancement, manufacturing is experiencing substantial transformations driven by technological innovations, societal demands, and a heightened focus on sustainability. A vital aspect of this evolution is the integration of Artificial Intelligence (AI) in intelligent manufacturing systems, which form the foundational structures of production processes. The advent of Industry 4.0/5.0 has initiated a new epoch characterized by the convergence of digitalization, automation, cobots, and data analytics, with AI playing a central role in enhancing these technologies.

The application of AI in manufacturing methodologies has been pivotal in improving efficiency, precision, and adaptability. AI-driven technologies such as machine learning, predictive analytics, generative intelligence, and autonomous systems are being leveraged by researchers and practitioners to redefine traditional manufacturing paradigms and to integrate these technologies into manufacturing systems, such as in the case of lean manufacturing. These innovations facilitate real-time decision-making, optimize production processes, and enable the development of smart factories that can respond dynamically to changing conditions. Sustainability, a crucial issue within the industry, is recurrently addressed through AI applications that promote green manufacturing practices and innovative process designs. AI helps monitor and reduce resource consumption, manage waste, and optimize energy usage, thus supporting regulatory and societal-driven green initiatives.

This Special Issue of Electronics seeks to explore and present the latest findings on the applications of AI in intelligent manufacturing, highlighting key trends shaping this essential industry's future. Contributions are invited that investigate these topics, providing a detailed understanding of how AI influences the development of manufacturing methodologies. The goal is to offer a comprehensive perspective on how AI-integrated manufacturing processes and systems can align with waste reduction, quality control, and sustainability, thereby promoting a more responsible and eco-friendly industry. To advance the state of the art and disseminate recent developments in AI applications in manufacturing, this Special Issue aims to gather substantial contributions through high-quality original research or review articles, ensuring widespread dissemination via the MDPI’s open access platform after an anonymous peer-review process. Relevant topics include, but are not limited to, the following:

  • Applied AI and ML in manufacturing; 
  • Sustainable manufacturing enabled by AI and ML; 
  • Quality control via AI and ML;
  • Integration of Lean Six Sigma with AI and ML;
  • Waste reduction via AI and ML; 
  • Integration of lean manufacturing with AI and ML; 
  • AI in the manufacturing enterprise;
  • Integration of supply chain logistics and management with AI and ML.

Dr. Mohammad Shahin
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. Electronics 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

  • lean AI
  • sustainable AI
  • generative AI
  • Industry 4.0/5.0
  • machine learning

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Published Papers (2 papers)

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Research

37 pages, 732 KiB  
Article
Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain
by Simon Knollmeyer, Oğuz Caymazer and Daniel Grossmann
Electronics 2025, 14(11), 2102; https://doi.org/10.3390/electronics14112102 - 22 May 2025
Abstract
Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and [...] Read more.
Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and evaluate GraphRAG, leveraging graph-based document structuring and a keyword-based semantic linking mechanism to improve retrieval quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, and a newly developed manufacturing dataset, demonstrates consistent performance gains over a naive RAG baseline across both retrieval and generation metrics. The results indicate that GraphRAG improves Context Relevance metrics, with task-dependent optimizations for chunk size, keyword density, and top-k retrieval further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured retrieval strategy, highlighting its advantages in complex reasoning tasks. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Intelligent Manufacturing)
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Graphical abstract

17 pages, 6015 KiB  
Article
Process Monitoring of One-Shot Drilling of Al/CFRP Aeronautical Stacks Using the 1DCAE-GMM Framework
by Giulio Mattera, Maria Grazia Marchesano, Alessandra Caggiano, Guido Guizzi and Luigi Nele
Electronics 2025, 14(9), 1777; https://doi.org/10.3390/electronics14091777 - 27 Apr 2025
Viewed by 251
Abstract
This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force [...] Read more.
This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force and torque signals at a 10 kHz sampling rate during the drilling process. These signals are employed for real-time process monitoring, focusing on material change detection and anomaly identification, where anomalies are defined as holes that fail to meet predefined quality criteria. An innovative approach based on unsupervised learning is proposed to enable automatic material change identification, signal segmentation, feature extraction, and hole quality assessment. Specifically, a semi-supervised approach based on a Gaussian Mixture Model (GMM) and 1D Convolutional AutoEncoder (1D-CAE) is employed to detect deviations from normal drilling conditions. The proposed method is benchmarked against state-of-the-art supervised techniques, including logistic regression (LR) and Support Vector Machines (SVMs). Results show that these traditional models struggle with class imbalance, leading to overfitting and limited generalisation, as reflected by the F1 scores of 0.78 and 0.75 for LR and SVM, respectively. In contrast, the proposed semi-supervised approach improves anomaly detection, achieving an F1 score of 0.87 by more effectively identifying poor-quality holes. This study demonstrates the potential of deep learning-based semi-supervised methods for intelligent process monitoring, enabling adaptive control in the drilling process of hybrid stacks and detecting anomalous holes. While the proposed approach effectively handles small and imbalanced datasets, further research into the application of generative AI could enhance performance, aiming for F1 scores above 0.90, thereby supporting adaptation in real industrial environments with high performance. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Intelligent Manufacturing)
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