2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Abstract
The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML in industrial settings still faces critical challenges, including the complexity of industrial big data, effective data management, integration with heterogeneous sensing and control systems, and the demand for trustworthy, explainable, and reliable operation in high-stakes industrial environments. In this roadmap, we present a comprehensive perspective on the foundations, applications, and emerging directions of AI and ML in smart manufacturing. It is structured in three parts. The first highlights the foundations and trends that frame the evolution of AI in smart manufacturing. The second focuses on key topics where AI is already enabling advances, including industrial big data analytics, advanced sensing and perception, autonomous systems, additive and laser-based manufacturing, digital twins, robotics, supply chain and logistics optimization, and sustainable manufacturing. The third section explores non-traditional ML approaches that are opening new frontiers, such as physics-informed AI, generative AI, semantic AI, advanced digital twins, explainable AI, RAMS, data-centric metrology, LLMs, and foundation models for highly connected and complex manufacturing systems. By identifying both opportunities and remaining barriers across these areas, this roadmap outlines the advances needed in methods, integration strategies, and industrial adoption. We hope this roadmap will serve as a guide for researchers, engineers, and practitioners to accelerate innovation, align academic and industrial priorities, and ensure that AI-driven smart manufacturing delivers reliable, sustainable, and scalable impact for the future of manufacturing ecosystems.
Cite
@article{arxiv.2605.00839,
title = {2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing},
author = {Jay Lee and Hanqi Su and Marco Macchi and Adalberto Polenghi and Wei Wu and Zhiheng Zhao and George Q. Huang and Kiva Allgood and Devendra Jain and Benedikt Gieger and Vibhor Pandhare and Soumyabrata Bhattacharjee and Ram Mohril and Lingbao Kong and Qiyuan Wang and Xinlan Tang and Sungjong Kim and Chan Hee Park and Byeng D. Youn and Guo Dong Goh and Xi Huang and Wai Yee Yeong and Yung C Shin and He Zhang and Zitong Wang and Fei Tao and Jagjit Singh Srai and Satyandra K. Gupta and Byung Gun Joung and Albin John and John W. Sutherland and Sang Won Lee and Olga Fink and Vinay Sharma and Faez Ahmed and Wei Chen and Mark Fuge and Arild Waaler and Martin G. Skjæveland and Dimitris Kyritsis and Wei Chen and VispiNevile Karkaria and Yi-Ping Chen and Ying-Kuan Tsai and Joseph Cohen and Xun Huan and Jing Lin and Liangwei Zhang and Gregory W. Vogl and Aaron W. Cornelius and Xiaodong Jia and Dai-Yan Ji and Takanobu Minami and Ruoxin Wang},
journal= {arXiv preprint arXiv:2605.00839},
year = {2026}
}
Comments
This paper has been accepted for publication in the Journal Machine Learning: Engineering