Abstract Additive manufacturing (AM) has emerged as a promising alternative to conventional manufacturing techniques. Among the various AM processes, laser powder bed fusion (L-PBF) has gained significant attention for fabricating metallic materials, particularly Ti-based alloys. Among these, Ti-6Al-4V stands out as one of the most widely used alloys in high-tech industries. Despite its advantages, L-PBF of Ti-6Al-4V faces several challenges commonly associated with this technique, including internal defects, poor surface quality, metastable microstructures, and high residual stresses. These factors, along with some others e.g. build orientation, complex geometries, irregular and reused powder feedstock, can significantly impact the fatigue performance of this material under complex dynamic loading conditions. This review examines how L-PBF process parameters and post-processing strategies can be tailored to control these factors and systematically elucidates the critical role of each of them on fatigue behavior. Furthermore, this work addresses key fatigue mechanisms and reviews fatigue life prediction approaches ranging from conventional methods to emerging data-driven techniques such as machine learning, alongside modeling strategies for realistic L-PBF Ti-6Al-4V components. The insights presented here offer valuable guidance for future research and technological advancements in this field.
Vafaei et al. (Tue,) studied this question.