2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910184727642112 |
|---|---|
| author | Lee, Jay Su, Hanqi Macchi, Marco Polenghi, Adalberto Wu, Wei Zhao, Zhiheng Huang, George Q. Allgood, Kiva Jain, Devendra Gieger, Benedikt Pandhare, Vibhor Bhattacharjee, Soumyabrata Mohril, Ram Kong, Lingbao Wang, Qiyuan Tang, Xinlan Kim, Sungjong Park, Chan Hee Youn, Byeng D. Goh, Guo Dong Huang, Xi Yeong, Wai Yee Shin, Yung C Zhang, He Wang, Zitong Tao, Fei Srai, Jagjit Singh Gupta, Satyandra K. Joung, Byung Gun John, Albin Sutherland, John W. Lee, Sang Won Fink, Olga Sharma, Vinay Ahmed, Faez Chen, Wei Fuge, Mark Waaler, Arild Skjæveland, Martin G. Kyritsis, Dimitris Chen, Wei Karkaria, VispiNevile Chen, Yi-Ping Tsai, Ying-Kuan Cohen, Joseph Huan, Xun Lin, Jing Zhang, Liangwei Vogl, Gregory W. Cornelius, Aaron W. Jia, Xiaodong Ji, Dai-Yan Minami, Takanobu Wang, Ruoxin |
| author_facet | Lee, Jay Su, Hanqi Macchi, Marco Polenghi, Adalberto Wu, Wei Zhao, Zhiheng Huang, George Q. Allgood, Kiva Jain, Devendra Gieger, Benedikt Pandhare, Vibhor Bhattacharjee, Soumyabrata Mohril, Ram Kong, Lingbao Wang, Qiyuan Tang, Xinlan Kim, Sungjong Park, Chan Hee Youn, Byeng D. Goh, Guo Dong Huang, Xi Yeong, Wai Yee Shin, Yung C Zhang, He Wang, Zitong Tao, Fei Srai, Jagjit Singh Gupta, Satyandra K. Joung, Byung Gun John, Albin Sutherland, John W. Lee, Sang Won Fink, Olga Sharma, Vinay Ahmed, Faez Chen, Wei Fuge, Mark Waaler, Arild Skjæveland, Martin G. Kyritsis, Dimitris Chen, Wei Karkaria, VispiNevile Chen, Yi-Ping Tsai, Ying-Kuan Cohen, Joseph Huan, Xun Lin, Jing Zhang, Liangwei Vogl, Gregory W. Cornelius, Aaron W. Jia, Xiaodong Ji, Dai-Yan Minami, Takanobu Wang, Ruoxin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00839 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing Lee, Jay Su, Hanqi Macchi, Marco Polenghi, Adalberto Wu, Wei Zhao, Zhiheng Huang, George Q. Allgood, Kiva Jain, Devendra Gieger, Benedikt Pandhare, Vibhor Bhattacharjee, Soumyabrata Mohril, Ram Kong, Lingbao Wang, Qiyuan Tang, Xinlan Kim, Sungjong Park, Chan Hee Youn, Byeng D. Goh, Guo Dong Huang, Xi Yeong, Wai Yee Shin, Yung C Zhang, He Wang, Zitong Tao, Fei Srai, Jagjit Singh Gupta, Satyandra K. Joung, Byung Gun John, Albin Sutherland, John W. Lee, Sang Won Fink, Olga Sharma, Vinay Ahmed, Faez Chen, Wei Fuge, Mark Waaler, Arild Skjæveland, Martin G. Kyritsis, Dimitris Chen, Wei Karkaria, VispiNevile Chen, Yi-Ping Tsai, Ying-Kuan Cohen, Joseph Huan, Xun Lin, Jing Zhang, Liangwei Vogl, Gregory W. Cornelius, Aaron W. Jia, Xiaodong Ji, Dai-Yan Minami, Takanobu Wang, Ruoxin Artificial Intelligence Machine Learning 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. |
| title | 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.00839 |