数字孪生在金属增材制造中的应用及研究进展

杜玉静

粉末冶金技术 ›› 2026, Vol. 44 ›› Issue (4) : 598 -620.

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粉末冶金技术 ›› 2026, Vol. 44 ›› Issue (4) : 598 -620. DOI: 10.19591/j.cnki.cn11-1974/tf.2026050011
非晶和高熵合金专栏

数字孪生在金属增材制造中的应用及研究进展

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Application and research progress of digital twin in metal additive manufacturing

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摘要

数字孪生技术因其在智能制造领域具有独特的优势而备受关注,数字孪生的概念模型、理论框架及实际部署已经得到了深入的研究与广泛的应用。数字孪生在金属增材制造中的应用日益受到重视,其作为连接物理世界与数字世界的桥梁,通过集成多物理场仿真、物联网感知、人工智能及大数据分析技术,构建与物理实体精准映射的虚拟模型,实现金属增材制造生产过程的实时监测,达成对材料、工艺、结构等全流程的精准调控与闭环优化。本文讨论了数字孪生技术与金属增材制造适配性,研究了数字孪生在金属增材制造中的核心应用场景,并梳理了数字孪生与金属增材制造面临的挑战以及发展趋势。

Abstract

Digital twin technology has attracted the extensive attention owing to the unique advantages in intelligent manufacturing. In-depth research and widespread applications have been carried out on the conceptual models, theoretical frameworks, and practical deployment. The application of digital twins in metal additive manufacturing is receiving the increasing attention, serving as a bridge connecting the physical and digital worlds. By integrating the multi-physics simulation, internet of things perception, artificial intelligence, and big data analysis technologies, the virtual models precisely mapped to physical entities are constructed to achieve the real-time monitoring of the metal additive manufacturing production process, and to achieve the precise regulation and closed-loop optimization of the entire process, including materials, processes, and structures. The compatibility of digital twin technology with metal additive manufacturing was discussed in this article, the application scenarios of digital twin in metal additive manufacturing were investigated, and the challenges and development trends faced by digital twin and metal additive manufacturing were summarized.

关键词

金属增材制造 / 人工智能 / 数字孪生 / 机器学习 / 多物理场仿真

Key words

metal additive manufacturing / artificial intelligence / digital twin / machine learning / multi-physics simulation

引用本文

引用格式 ▾
杜玉静. 数字孪生在金属增材制造中的应用及研究进展[J]. 粉末冶金技术, 2026, 44(4): 598-620 DOI:10.19591/j.cnki.cn11-1974/tf.2026050011

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基金资助

河南省高等学校重点科研项目(26A520003)

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