2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
2026年智能制造人工智能与机器学习路线图
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. 摘要： 人工智能（AI）和机器学习（ML）的演进正在重塑智能制造，通过为整个工业价值链提供效率、适应性和自主性方面的新能力，推动行业变革。
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. 然而，AI 和 ML 在工业环境中的部署仍面临严峻挑战，包括工业大数据的复杂性、有效的数据管理、与异构传感和控制系统的集成，以及在高风险工业环境中对可信、可解释和可靠运行的需求。
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. 在本路线图中，我们从基础、应用和新兴方向三个维度，对智能制造中的 AI 和 ML 进行了全面阐述。报告分为三个部分。
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. 第一部分重点介绍了构建智能制造中 AI 演进的基础和趋势。第二部分聚焦于 AI 已经推动进步的关键领域，包括工业大数据分析、先进传感与感知、自主系统、增材与激光制造、数字孪生、机器人技术、供应链与物流优化以及可持续制造。
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. 第三部分探讨了正在开辟新前沿的非传统机器学习方法，例如物理信息 AI、生成式 AI、语义 AI、先进数字孪生、可解释 AI、可靠性/可用性/可维护性/安全性（RAMS）、以数据为中心的计量学、大语言模型（LLMs）以及用于高度互联和复杂制造系统的基础模型。
By identifying both opportunities and remaining barriers across these areas, this roadmap outlines the advances needed in methods, integration strategies, and industrial adoption. 通过识别这些领域中的机遇与现有障碍，本路线图概述了在方法论、集成策略和工业应用方面所需的进步。
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. 我们希望这份路线图能为研究人员、工程师和从业者提供指导，以加速创新，协调学术界与工业界的优先事项，并确保人工智能驱动的智能制造能为未来制造生态系统带来可靠、可持续且可扩展的影响。