AI与机器学习正重塑智能制造，该路线图系统梳理了关键技术与挑战。
2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
- 分三部分构建智能制造AI发展框架：基础趋势、应用领域与新兴方法。
- 涵盖工业大数据、数字孪生、自主系统等10大关键方向，推动制造智能化。
- 聚焦可信AI、生成式模型等前沿技术，适合研究者与产业工程师参考。
人工智能（AI）与机器学习（ML）的演进正在重塑智能制造，为工业价值链中的效率、适应性和自主性提供新能力。然而，其在工业场景的部署仍面临诸多挑战，包括工业大数据的复杂性、有效数据管理、异构传感与控制系统集成，以及高风险工业环境中对可信、可解释、可靠运行的需求。本文提出一份全面的路线图，涵盖智能制造业中AI与ML的基础、应用与未来方向。第一部分聚焦塑造AI演进的基础与趋势；第二部分重点阐述当前已推动进展的关键领域，包括工业大数据分析、先进感知与识别、自主系统、增材与激光制造、数字孪生、机器人、供应链与物流优化、可持续制造；第三部分探索非传统机器学习方法开启的新前沿，如物理信息引导的AI、生成式AI、语义AI、高级数字孪生、可解释AI、RAMS、以数据为中心的计量学、大语言模型（LLMs）及面向高度互联复杂制造系统的基础模型。通过识别各领域的机遇与障碍，本路线图明确了方法、集成策略与工业采纳所需的技术突破，旨在为研究人员、工程师和从业者提供指引，加速创新，协调学术与产业目标，确保AI驱动的智能制造在未来制造生态中实现可靠、可持续且可扩展的影响。
原文摘要 · Abstract (English)
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.
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