2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing

arXiv cs.AI / 5/5/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisIndustry & Market MovesModels & Research

共有:

Key Points

The roadmap explains how AI and ML are transforming smart manufacturing by enabling greater efficiency, adaptability, and autonomy across industrial value chains, while noting persistent real-world deployment challenges.
It identifies key application areas where progress is already occurring, including industrial big data analytics, advanced sensing and perception, autonomous systems, digital twins, robotics, and optimization for supply chains and logistics, as well as sustainable manufacturing.
The document highlights the engineering hurdles to overcome in high-stakes environments, such as managing complex industrial data, integrating with heterogeneous sensing/control systems, and delivering trustworthy, explainable, and reliable AI operations.
It also surveys emerging, non-traditional ML directions—covering physics-informed AI, generative AI, semantic AI, explainable AI, RAMS, data-centric metrology, LLMs, and foundation models—to open new frontiers for connected and complex manufacturing.
Overall, the roadmap aims to align academic and industrial priorities and provide guidance on the methods, integration strategies, and adoption steps needed for scalable and sustainable AI-driven manufacturing impact.

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.