面向Ti-6Al-4V合金增材制造的机器学习研究进展

doi:10.3901/JME.260072

机械工程学报 ›› 2026, Vol. 62 ›› Issue (3): 86-103.doi: 10.3901/JME.260072

• 特邀专栏：增材制造技术 • 上一篇

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面向Ti-6Al-4V合金增材制造的机器学习研究进展

詹远新¹, 林勤龙¹, 刘洋^1,2, 高英³, 吴剑明⁴, 张嘉振⁵

1. 浙江师范大学数学科学学院金华 321004;
2. 浙江师范大学杭州自动化学院杭州 311231;
3. 浙江师范大学工学院金华 321004;
4. 浙江师范大学计算机科学与技术学院金华 321004;
5. 中国商用飞机有限责任公司北京民用飞机技术研究中心北京 102200

修回日期:2025-09-05 接受日期:2025-10-31 发布日期:2026-03-25
作者简介:詹远新,男,2000年出生。主要研究方向为机器学习和优化。E-mail:zyxjsw0609@zjnu.edu.cn
林勤龙,男,1999年出生,博士研究生。主要研究方向为多智能体系统、分布式优化和机器学习。E-mail:linqinlong@zjnu.edu.cn
刘洋(通信作者),男,1982年出生,博士,教授,博士研究生导师。主要研究方向为人工智能基础理论,网络控制与优化。E-mail:liuyang@zjnu.edu.cn
高英,男,1991年出生,博士,讲师。主要研究方向为金属增材制造疲劳损伤容限分析。E-mail:gyhust@163.com
吴剑明,男,1976年出生,博士,教授,博士研究生导师。主要研究方向为多模态大模型、元宇宙通用智能体、虚拟课堂、智慧养老。
E-mail:swordwu@zjnu.edu.cn
张嘉振,男,1961年出生,博士,教授,博士研究生导师。主要研究方向为增材制造,金属材料疲劳,机器学习,复合材料结构强度研究和飞行器结构设计。
E-mail:zhangjiazhen2009@126.com

Advances in Machine Learning for Additive Manufacturing of Ti-6Al-4V

ZHAN Yuanxin¹, LIN Qinlong¹, LIU Yang^1,2, GAO Ying³, WU Jianming⁴, ZHANG Jiazheng⁵

1. School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004;
2. Hangzhou School of Automation, Zhejiang Normal University, Hangzhou 311231;
3. College of Engineering, Zhejiang Normal University, Jinhua 321004;
4. School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004;
5. Beijing Aeronautical Science and Technology Research Institute, Commercial Aircraft Corporation of China Ltd(COMAC), Beijing 102200

Revised:2025-09-05 Accepted:2025-10-31 Published:2026-03-25
Supported by:
国家重点研发计划(2024YFE0214000)、国家自然科学基金(62173308)、浙江省自然科学基金(LRG25F030002)、浙江省“领雁”计划(2025C01056)、金华市科技计划(2022-1-042)和江苏省自然科学基金(BK20240009)资助项目。

摘要/Abstract

摘要： 随着增材制造技术的迅猛发展，Ti-6Al-4V合金在航空航天领域展现出巨大潜力。因此，基于机器学习的Ti-6Al-4V合金增材制造已成为关键研究前沿。系统综述了利用支持向量回归、随机森林、高斯过程和人工神经网络等机器学习模型，预测合金疲劳寿命、残余应力和拉伸强度等关键宏观性能指标的最新战果。尽管相关研究已取得一定进展，现有模型仍面临数据稀缺、物理可解释性差以及跨工艺条件泛化能力弱等局限。为应对这些挑战，我们提出“数据—算法—机理”三元框架，融合物理信息神经网络、强化学习、元学习与数据增强策略，以提升模型的预测精度、鲁棒性与可迁移性，旨在为研究人员提供实用指导，加速面向Ti-6Al-4V合金的机器学习增材制造技术发展。

关键词: 机器学习, 增材制造, 模型预测, Ti-6Al-4V合金, 宏观性能

Abstract: With the rapid advancement of additive manufacturing (AM), Ti-6Al-4V alloy has demonstrated tremendous potential in the aerospace sector. Consequently, machine-learning-based additive manufacturing of Ti-6Al-4V alloy has become a critical research frontier. This review summarizes recent progress in predicting the fatigue life, residual stress, and tensile strength of alloy using machine-learning models such as support-vector regression, random forest, Gaussian process, and artificial neural networks. Despite notable achievements, current models still suffer from limited data, poor physical interpretability and weak generalization across varying process conditions. To address these challenges, we propose a “data-algorithm-mechanism” triad framework that integrates physics-informed neural networks, reinforcement learning, meta-learning and data-augmentation strategies to improve predictive accuracy, robustness and transferability. The review aims to provide practical guidance for researchers and to accelerate the development of machine-learning-enabled additive manufacturing technologies for Ti-6Al-4V alloy.

Key words: machine learning, additive manufacturing, model prediction, Ti-6Al-4V alloy, macroscopic performance

中图分类号:

V252

詹远新, 林勤龙, 刘洋, 高英, 吴剑明, 张嘉振. 面向Ti-6Al-4V合金增材制造的机器学习研究进展[J]. 机械工程学报, 2026, 62(3): 86-103.

ZHAN Yuanxin, LIN Qinlong, LIU Yang, GAO Ying, WU Jianming, ZHANG Jiazheng. Advances in Machine Learning for Additive Manufacturing of Ti-6Al-4V[J]. Journal of Mechanical Engineering, 2026, 62(3): 86-103.

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面向Ti-6Al-4V合金增材制造的机器学习研究进展

Advances in Machine Learning for Additive Manufacturing of Ti-6Al-4V

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