AI-Driven Smart Manufacturing: Bridging Data Innovation, Industrial Practice, and Ethical Intelligence

Dear Colleagues,

The manufacturing sector is undergoing a profound transformation, driven by the convergence of artificial intelligence (AI), machine learning, and advanced data analytics. This shift toward intelligent, data-centric manufacturing is redefining how products are designed, produced, and delivered—enabling higher levels of efficiency, flexibility, and customisation.

This Special Issue aims to present leading-edge research and practical innovations at the intersection of AI, data science, and manufacturing systems. We welcome contributions that explore how intelligent technologies are being deployed to solve real-world manufacturing challenges, enhance operational decision-making, and create more adaptive, resilient, and human-centric production environments.

We are particularly interested in submissions that highlight the following:

• The use of machine learning for process optimisation, predictive maintenance, and quality assurance;
• Emerging technologies such as digital twins, cyber-physical systems, and the Industrial Internet of Things (IIoT);
• Applications of computer vision, neural networks, reinforcement learning, and edge computing in production settings;
• Human–AI collaboration, including explainability, trust, and the role of operators in AI-assisted environments;
• Ethical considerations and sustainable practices in data-driven manufacturing.

This Special Issue also seeks to bridge the gap between academic research and industrial application, offering a platform for knowledge exchange among researchers, practitioners, and industry stakeholders. We are especially keen on studies that provide evidence of real-world impact, such as improvements in productivity, quality, sustainability, or cost-effectiveness through intelligent systems.

Submissions may include original research articles, comprehensive reviews, case studies, and technical communications. Contributions that present validated tools, scalable approaches, or frameworks for responsible AI integration in manufacturing are highly encouraged.

By curating a collection of forward-looking and grounded research, this Special Issue will serve as a key resource for those seeking to understand and implement AI and data-driven solutions in modern manufacturing, fostering a more intelligent, ethical, and resilient industrial future.

Dr. Saeid Lotfian
Dr. Saleh Seyedzadeh
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. Journal of Manufacturing and Materials Processing 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.

• artificial Intelligence (AI) in manufacturing
• data-driven manufacturing
• intelligent manufacturing
• predictive maintenance and quality assurance
• digital twins and cyber-physical systems
• industrial internet of things (IIoT)