2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Jay Lee1,32,33, Hanqi Su1,32,33, Marco Macchi2, Adalberto Polenghi2, Wei Wu³, Zhiheng Zhao3, George Q.
Huang³, Kiva Allgood4, Devendra Jain4, Benedikt Gieger4, Vibhor Pandhare³¹, Soumyabrata
Bhattacharjee5, Ram Mohril5, Lingbao Kong6, Qiyuan Wang6, Xinlan Tang6, Sungjong Kim7, Chan Hee
Park8, Byeng D. Youn7,9, Guo Dong Goh10, Xi Huang¹⁰, Wai Yee Yeong10,11, Yung C Shin¹², He Zhang¹³, Zitong Wang13, Fei Tao13,14, Jagjit Singh Srai15, Satyandra K. Gupta16, Byung Gun Joung17, Albin John17, John W. Sutherland17, Sang Won Lee18, Olga Fink19, Vinay Sharma¹⁹, Faez Ahmed20, Wei “Wayne” Chen21, Mark Fuge²², Arild Waaler23, Martin G. Skjæ veland23, Dimitris Kyritsis23, Wei Chen24, Vispi Nevile Karkaria24, Yi-Ping Chen24, Ying-Kuan Tsai24, Joseph Cohen²⁵, Xun Huan26, Jing (Janet) Lin27, Liangwei Zhang28, Gregory W. Vogl29, Aaron W. Cornelius29, Xiaodong Jia³⁰, Dai-Yan Ji1, Takanobu Minami1, Ruoxin Wang¹
1 Center for Industrial Artificial Intelligence, Department of Mechanical Engineering, University of Maryland, College Park, 20742, United States of America 2 Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy 3 Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, People’s Republic of China 4 Centre for Advanced Manufacturing & Supply Chains, World Economic Forum, Cologny/Geneva, Switzerland 5 Department of Mechanical Engineering, Indian Institute of Technology Indore, Indore, India 6 Future Information Innovative College, Fudan University, Shanghai, China 7 Department of Mechanical Engineering, Seoul National University, Seoul 08826, Republic of Korea 8 Department of Mechanical and Information Engineering, University of Seoul, Seoul 02556, Republic of Korea 9 Onepredict Corp., Seoul 06105, Republic of Korea 10 School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore 11 Singapore Centre for 3D Printing, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore 12 Mechanical Engineering, Purdue University, West Lafayette, Indiana, U.S.A. 13 Digital Twin International Research Center, International Institute for Interdisciplinary and Frontiers, Beihang University, Beijing, China 14 School of Automation Science and Electrical Engineering, Beihang University, Beijing, China 15 Department of Engineering, University of Cambridge, UK 16 Center for Advanced Manufacturing, University of Southern California, Los Angeles, CA, USA 17 School of Sustainability Engineering and Environmental Engineering, Purdue University, West Lafayette, USA 18 School of Mechanical Engineering, Sungkyunkwan University, Suwon-si, Republic of Korea 19 Intelligent Maintenance and Operations Systems, EPFL, Lausanne, Switzerland 20 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, USA 21
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, USA 22 Department of Mechanical and Process Engineering, ETH Zürich, Switzerland
Journal XX (XXXX) XXXXXX A Author et al
Department of Informatics, University of Oslo, Norway Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ, USA 26 Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA 27 Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden 28 Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China 29 Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, USA 30 Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, USA 31 Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai, India
32 Guest Editors of the Roadmap. 33 Author to whom any correspondence should be addressed.
E-mails: leejay@umd.edu, hanqisu@umd.edu
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 machine learning approaches that are opening new frontiers, such as physics-informed AI, generative AI, semantic AI, advanced digital twins, explainable AI, RAMS, data-centric metrology, large language models, 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.
Contents
| Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing | |
|---|---|
| 0 | Introduction |
| Section 1 | Foundations and Trends in AI for Smart Manufacturing |
| 1 | The evolution of AI and machine learning in smart manufacturing. |
| 2 | The importance of AI-driven efficiency, adaptability, and automation for future manufacturing. |
| 3 | The Outlook of AI in Manufacturing and Value Chains. |
| Section 2 | Key Topics in AI-Enabled Smart Manufacturing |
| 4 | Streamlining Industrial Big Data Analytics for Smart Manufacturing |
| 5 | Advanced Sensing, Perception, and Analytics for Manufacturing |
| 6 | AI-Enabled Autonomous Manufacturing |
| 7 | Additive Manufacturing |
| 8 | Machine Learning in Laser-based Manufacturing |
| 9 | Digital Twin in Smart Manufacturing |
| 10 | AI for Smart Supply Chain and Logistics |
| 11 | AI-Enhanced Robotics and Autonomous Systems |
| 12 | AI-enabled Sustainable Manufacturing |
| Section 3 | Non-Traditional Machine Learning Techniques for Smart Manufacturing |
| 13 | Machine Learning and Deep Learning for Manufacturing |
| 14 | Physics Informed Machine Learning through Inductive Bias |
| 15 | Generative AI for Design and Manufacturing |
| 16 | Semantic Framework Enabling Machine Learning in Manufacturing |
| 17 | Physics-Based Predictive Control and Real-Time Decisions for Digital Twin–Enabled Autonomous Manufacturing |
| 18 | Trustworthy AI for Manufacturing |
| 19 | Enabling Dependability in Smart Manufacturing: RAMS and AI/ML Integration |
| 20 | Data Centric Metrology in Future Manufacturing |
| 21 | Non-Traditional Machine Learning for Highly Connected and Complex Manufacturing Systems |