ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
This article is part of the Research TopicRobust and Secure AI Systems for Learning from Heterogeneous DataView all 3 articles
Generative and Predictive AI for Digital Twin Systems in Manufacturing
Provisionally accepted
- 1University of Warwick, Coventry, United Kingdom
- 2School of Computer Science and Digital Technologies, Aston University, Birmingham, United Kingdom
- 3School of Mathematics & Statistics, University of Glasgow, Glasgow, United Kingdom
- 4School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
- 5Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
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The integration of Artificial Intelligence (AI) and Digital Twin (DT) technology is reshaping modern manufacturing by enabling real-time monitoring, predictive maintenance, and intelligent process optimisation. This paper presents the design and partial implementation of an AI-enabled Digital Twin System (AI-DT) for manufacturing, focusing on the deployment of Generative AI (GAI) and Predictive AI (PAI) modules. The GAI component is used to augment training data, perform geometric inspection, and generate 3D virtual testing environments from multiview video input. Meanwhile, PAI leverages sensor data to enable proactive defect detection and predictive quality analysis in welding processes. These integrated capabilities significantly enhance the system's ability to anticipate issues and support decision-making. While the framework also envisions incorporating Explainable AI (EAI), Context-Aware AI (CAI), and Agentic AI (AAI) for future extensions, the current work establishes a robust foundation for scalable, intelligent digital twin systems in smart manufacturing. Our findings contribute toward improving operational efficiency, quality assurance, and early-stage digital-physical convergence.
Keywords: AI-enabled Digital Twin, Generative AI, Predictive AI, Quality Assurance, Smart manufacturing
Received: 27 Jun 2025; Accepted: 17 Nov 2025.
Copyright: © 2025 Dai, Zhao, Yu, Franciosa and Ceglarek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dan Dai, daidanjune@hotmail.com
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