Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

作者：Zhixin Zhan, Xiaofan He, Dingcheng Tang, Linwei Dang, Ao Li, Qianyu Xia, Filippo Berto, Hua Li · 发表于：Fatigue & Fracture of Engineering Materials & Structures · 年份：2023 · DOI：10.1111/ffe.14152 · 被引用次数：41 · 研究领域：Additive Manufacturing Materials and Processes、Additive Manufacturing and 3D Printing Technologies、Welding Techniques and Residual Stresses

Abstract Additive manufacturing (AM) has emerged as a very promising technology for producing complex metallic components with enhanced design flexibility. However, the mechanical properties and fatigue behavior of AM metals differ significantly from conventionally manufactured materials, thereby presenting challenges in accurately predicting their fatigue life. This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of AM metals, with a particular emphasis on machine learning (ML) modeling techniques. This review recalls recent developments and achievements in fatigue characteristics of AM metals, ML‐based approaches for fatigue life prediction of AM metals, and non‐ML‐based methodologies for the same purpose. In particular, some commonly used regression and classification techniques for fatigue evaluation of AM metals are summarized and elaborated. The study intends to furnish researchers, engineers, and practitioners in the field of AM with a guidance for the accurate and efficient prediction of fatigue life in AM metal components.