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

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. Background and motivation are presented for the fatigue life prediction of AM metals.Fatigue characteristics of AM metals and influencing factors are extensively reviewed.State-of-art ML-based methods for fatigue life prediction of AM metals are summarized.Challenges and opportunities are concluded in ML-based fatigue prediction of AM metals.

Automated summary

This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of additive manufacturing (AM) metals, with a particular emphasis on machine learning (ML) modeling techniques. It summarizes the recent achievements in fatigue characteristics of AM metals, ML-based approaches for fatigue life prediction, and non-ML-based methodologies. The study aims to guide researchers and engineers in accurately and efficiently predicting fatigue life in AM metal components.