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Fatigue life assessment of metal additive manufacturing (AM) products has remained challenging due to the uncertainty of as–built defects, heterogeneity of the microstructure, and residual stress. In the past few years, many works have been conducted to develop models in order to predict fatigue life of metal AM samples by considering the existence of AM inherent defects. This review paper addresses the main issues regarding fatigue assessment of metal AM parts by considering the effect of defects and post processing strategies. Mechanisms that are contributing to the failure of metal AM samples are categorized and discussed in detail. Several modelling approaches exist in the case of fatigue life prediction. The common fatigue models that are compatible with AM properties are thoroughly explained by discussing the previous works and highlighting their major conclusions. In addition, the use of machine learning is identified as the future of metal AM fatigue life assessment due to their high performance. The main challenge of today's fatigue and fracture community was identified as the fatigue life estimation of complex geometries with the presence of different types of defects, anisotropic microstructure, and complex state of residual stress. This work proposes the available approaches to tackle this challenge.
Javidrad et al. (Thu,) studied this question.
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