NIST - National Institute of Standards and Technology

07/03/2026 | Press release | Archived content

2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing

Published
July 3, 2026

Author(s)

Gregory Vogl, Aaron Cornelius, Xiaodong Jia

Abstract

The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing (SM) 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 SM. It is structured in three parts. The first highlights the foundations and trends that frame the evolution of AI in SM. 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 (DTs), 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 DTs, explainable AI, reliability, availability, maintainability, and safety, 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 SM delivers reliable, sustainable, and scalable impact for the future of manufacturing ecosystems.
Citation
Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Volume
2
Publisher Info
IOP Publishing Ltd, Philadelphia, PA
Pub Type
Book Chapters

Keywords

smart manufacturing, artificial intelligence, machine learning, digital twin, industry 4.0, industrial large knowledge model, industrial artificial intelligence

Citation

Vogl, G. , Cornelius, A. and Jia, X. (2026), 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing, Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing, IOP Publishing Ltd, Philadelphia, PA, [online], https://doi.org/10.1088/3049-4761/ae5967, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960263 (Accessed July 7, 2026)
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