Predicting Fatigue Life of Additively Manufactured Metals Using Data-Driven and Defect-Based Models: a Comprehensive Review

Abstract

Additive manufacturing (AM) is a new trend in the production of lightweight, complex metallic components. Although AM metals often match or surpass the static mechanical properties of conventionally manufactured metals, their fatigue performance remains a significant challenge. Reduced fatigue resistance under cyclic loading limits their use in safety-critical applications. Despite extensive research, a comprehensive synthesis of fatigue data and mechanisms is still lacking. This review addresses this need by consolidating and critically evaluating the literature on the fatigue behavior of AM metals, with a focus on S–N responses and fatigue crack growth characteristics. The review covers major alloy systems produced by various AM techniques and examines how processing variables such as build orientation, processing parameters, and post-processing treatments affect fatigue performance. Comparative data are presented to highlight trends across alloys and manufacturing methods. The paper also analyses fatigue mechanisms and performance-limiting factors, offering insights for improving fatigue reliability in AM components. Finally, it reviews data-driven and physics-based modelling approaches for fatigue life prediction and discusses future challenges and directions for development in this field.