材料工程2026，Vol.54Issue(8)：91-105,15.DOI:10.11868/j.issn.1001-4381.2026.000186
机器学习在搅拌摩擦焊接与增材制造领域的应用现状与展望
Current application status and prospects of machine learning in friction stir welding and additive manufacturing
摘要
Abstract
Friction stir welding and its derived solid-state additive manufacturing technologies stand as one of the effective approaches to avoid melting defects and achieve the fabrication of high-performance lightweight alloy components.However,the friction stir welding and solid-state additive manufacturing processes involve complex thermo-mechanical-fluid-microstructure couplings,posing significant challenges to traditional trial-and-error methods for process optimization.The emergence of machine learning provides a transformative solution for process understanding and intelligent control in this field.This paper presents a systematic review of machine learning applications in friction stir welding and additive friction stir deposition.It categorizes and elaborates on the current research status and data processing strategies in aspects such as performance prediction,defect detection,and in-situ control.Addressing the limitations of purely data-driven models,it focuses on investigating different fusion paradigms of physics-informed machine learning and their cutting-edge applications.Finally,it points out that future research should concentrate on further developing generalizable prediction models,achieving real-time closed-loop intelligent control,and integrating active learning for autonomous process exploration,aiming to provide references for advancing these technologies toward intelligent and high-performance development.关键词
搅拌摩擦焊/搅拌摩擦增材制造/机器学习/工艺优化/过程监控/物理信息机器学习Key words
friction stir welding/friction stir additive manufacturing/machine learning/process optimization/process monitoring/physics-informed machine learning分类
矿业与冶金引用本文复制引用
石磊,戴国欣,张贤昆,武传松,颜世涛..机器学习在搅拌摩擦焊接与增材制造领域的应用现状与展望[J].材料工程,2026,54(8):91-105,15.基金项目
山东省自然科学基金优秀青年科学基金项目(ZR2024YQ020) （ZR2024YQ020）
国家自然科学基金项目(52275349,52035005) （52275349,52035005）
