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

doi:10.3901/JME.260072

Abstract

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

CLC Number:

V252

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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