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

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Guest Editor
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Interests: additive manufacturing; remanufacturing; energy materials; composites; joining; welding; structural integrity; fatigue; fracture; corrosion; failure
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Guest Editor
The Data Lab, University of Edinburgh, Edinburgh, UK
Interests: data science; AI applications

Special Issue Information

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

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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.

Keywords

  • 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)

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Published Papers (2 papers)

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Research

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26 pages, 2075 KB  
Article
Overall Equipment Effectiveness as a Strategic KPI in Intelligent Manufacturing: A Case Study in Plastic Injection Moulding
by Sonia Val, Nicolás Jiménez and María Pilar Lambán
J. Manuf. Mater. Process. 2026, 10(5), 159; https://doi.org/10.3390/jmmp10050159 - 30 Apr 2026
Viewed by 1131
Abstract
Intelligent manufacturing requires strategic performance indicators that link shop-floor performance with productivity and sustainability goals. This study examines Overall Equipment Effectiveness (OEE) as a strategic key performance indicator and applies it to a hydraulic plastic injection-moulding machine producing an automotive component. Production data [...] Read more.
Intelligent manufacturing requires strategic performance indicators that link shop-floor performance with productivity and sustainability goals. This study examines Overall Equipment Effectiveness (OEE) as a strategic key performance indicator and applies it to a hydraulic plastic injection-moulding machine producing an automotive component. Production data captured through a PLC-and-SQL-integrated digital monitoring system over 14 months were used to calculate monthly Availability, Performance, Quality, and OEE values and to identify the main sources of efficiency loss. The baseline period showed low OEE, driven mainly by unplanned downtime, minor stoppages, and cycle times above the 45 s target, whereas Quality remained consistently close to 100%. A diagnostic analysis combining production logs, downtime stratification, cycle-time records, and consultations with plant personnel was then used to define improvement actions. The implemented measures included preventive and predictive maintenance, process-parameter optimisation, operator training, and wider use of digital monitoring and analytics. In the post-improvement period, OEE increased markedly, downtime decreased, and cycle-time stability improved, reaching values close to world-class performance. The results confirm that OEE can function as a unifying KPI for intelligent manufacturing, supporting data-driven decision-making, continuous improvement, and more sustainable production. Full article
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Review

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55 pages, 3716 KB  
Review
Digital Enablers of the Circular Economy: A Systematic Review of Applications, Barriers, and Future Directions
by Parinaz Pourrahimian, Saleh Seyedzadeh, Behrouz Arabi, Daniel Kahani and Saeid Lotfian
J. Manuf. Mater. Process. 2026, 10(4), 112; https://doi.org/10.3390/jmmp10040112 - 25 Mar 2026
Cited by 1 | Viewed by 2991
Abstract
This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications [...] Read more.
This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article
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