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Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 16139

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Special Issue Editors

Prof. Dr. Chih-Hung Li

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Guest Editor

College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: intelligent manufacturing; robotics and automation; application of artificial intelligence; structural optimization; mechanism innovation; solid mechanics

Prof. Dr. Antonella Petrillo

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Guest Editor

Department of Engineering, University of Naples “Parthenope”, 80143 Napoli, Italy
Interests: smart manufacturing; digital transformation; sustainability; circular economy; automation systems
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Special Issue Information

Dear Colleagues,

We are thrilled to announce the call for papers for our upcoming Special Issue, which will focus on Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing.

In today's rapidly evolving technological landscape, Industry 4.0 and smart manufacturing stand at the forefront of innovation, reshaping traditional industrial processes and revolutionizing how goods are produced and serviced. Artificial Intelligence (AI) technologies are central to this transformation and hold immense potential for optimizing production processes, enhancing efficiency, and driving unprecedented levels of automation.

This Special Issue aims to explore the intersection of AI technologies and smart manufacturing, showcasing groundbreaking research, innovative methodologies, and practical applications poised to redefine the industrial landscape. From predictive maintenance and quality control to virtual metrology and autonomous robotics, we invite contributions highlighting the diverse array of AI-driven solutions shaping the future of manufacturing.

Topics of interest include, but are not limited to, the following:

AI-driven predictive maintenance and condition monitoring;
Autonomous robotic systems for manufacturing and logistics;
Intelligent process optimization and control;
Data-driven quality assurance and defect detection;
Adaptive scheduling and resource allocation;
Smart sensors and IoT integration for real-time monitoring and decision-making;
AI-enabled supply chain management and logistics optimization;
Human–robot collaboration and augmented reality interfaces;
Ethical considerations and societal implications of AI in manufacturing.

We welcome original research articles, review papers, case studies, and perspectives that offer valuable insights into the application of AI technologies in Industry 4.0 and smart manufacturing.

Warm regards,

Prof. Dr. Chih-Hung Li
Prof. Dr. Antonella Petrillo
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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

AI-driven manufacturing
Industry 4.0 innovation
smart factory solutions
automation and AI integration
intelligent process optimization

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Published Papers (3 papers)

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Research

30 pages, 2117 KB

Open AccessArticle

Automated Structuring and Analysis of Unstructured Equipment Maintenance Text Data in Manufacturing Using Generative AI Models: A Comparative Study of Pre-Trained Language Models

by Yongju Cho

Appl. Sci. 2026, 16(4), 1969; https://doi.org/10.3390/app16041969 - 16 Feb 2026

Abstract

Manufacturing companies face significant challenges in leveraging artificial intelligence for equipment management due to high infrastructure costs and limited availability of labeled data for failures. While most manufacturing AI applications focus on structured sensor data, vast amounts of unstructured textual information containing valuable maintenance knowledge remain underutilized. This study presents a practical generative AI-based framework for structured information extraction that automatically converts unstructured equipment maintenance texts into predefined semantic fields to support predictive maintenance in manufacturing environments. We adopted and evaluated three representative generative models—Bidirectional and Auto-Regressive Transformers (BART) with KoBART, Text-to-Text Transfer Transformer (T5) with pko-t5-base, and the large language model Qwen—to generate structured outputs by extracting three predefined fields: failed components, failure types, and corrective actions. The framework enables the structuring of equipment management text data from Manufacturing Execution Systems (MES) to build predictive maintenance support systems. We validated the approach using a large-scale MES dataset consisting of 29,736 equipment maintenance records from a major automotive parts manufacturer, from which curated subsets were used for model training and evaluation. Our methodology employs Generative Pre-trained Transformer 4 (GPT-4) for initial dataset construction, followed by domain expert validation to ensure data quality. The trained models achieved promising performance when evaluated using extraction-aligned metrics, including exact match (EM) and token-level precision, recall, and F1-score, which directly assess field-level extraction correctness. ROUGE scores are additionally reported as a supplementary indicator of lexical overlap. Among the evaluated models, Qwen consistently outperformed BART and T5 across all extracted fields. The structured outputs are further processed through domain-specific dictionaries and regular expressions to create a comprehensive analytical database supporting predictive maintenance strategies. We implemented a web-based analytics platform enabling time-series analysis, correlation analysis, frequency analysis, and anomaly detection for equipment maintenance optimization. The proposed system converts tacit knowledge embedded in maintenance texts into explicit, actionable insights without requiring additional sensor installations or infrastructure investments. This research contributes to the manufacturing AI field by demonstrating a comprehensive application of generative language models to equipment maintenance text analysis, providing a cost-effective approach for digital transformation in manufacturing environments. The framework’s scalability and cloud-based deployment model present significant opportunities for widespread adoption in the manufacturing sector, supporting the transition from reactive to predictive maintenance strategies. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing)

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19 pages, 3556 KB

Open AccessArticle

Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency

by Jose Isidro Hernández-Vega, Luis Alejandro Reynoso-Guajardo, Mario Carlos Gallardo-Morales, María Ernestina Macias-Arias, Amadeo Hernández, Nain de la Cruz, Jesús E. Soto-Soto and Carlos Hernández-Santos

Appl. Sci. 2024, 14(22), 10279; https://doi.org/10.3390/app142210279 - 8 Nov 2024

Cited by 5 | Viewed by 6531

Abstract

This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing)

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34 pages, 5941 KB

Open AccessArticle

Digital and Sustainable Transition in Textile Industry through Internet of Things Technologies: A Pakistani Case Study

by Antonella Petrillo, Mizna Rehman and Illaria Baffo

Appl. Sci. 2024, 14(13), 5380; https://doi.org/10.3390/app14135380 - 21 Jun 2024

Cited by 22 | Viewed by 8455

Abstract

The textile industry, a vital contributor to Pakistan’s economy, faces pressing challenges in transitioning towards sustainability amid global environmental concerns. This manuscript presents a comprehensive case study on the implementation of IoT-driven strategies in the Pakistani textile sector to achieve digital and sustainable transformation. The findings reveal that the implementation of IoT technologies facilitated real-time environmental monitoring, enabling compliance with regulatory standards, and fostering sustainable manufacturing practices. Ultimately, this manuscript offers valuable insights into the transformative potential of IoT technologies in driving sustainable practices in the textile industry. The case study serves as a benchmark for other textile-producing regions aiming to embark on a digital and sustainable journey. These findings hold significant implications for the ongoing dialogue on sustainable industrial development, providing valuable direction for policymakers and stakeholders in shaping a more resilient and ecologically conscious future. Future research should prioritize addressing issues like data confidentiality and interoperability while adhering to standard requirements. Additionally, exploring analytics and machine learning methods for predictive maintenance, optimized performance, and operational improvement is crucial. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing)

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