Defect-based fatigue life prediction of L-PBF additive manufactured metals

Metal additive manufacturing (AM) while offering advantages such as generating parts with intricate geometries, introduces challenges such as intrinsic defects. Since fatigue cracks often start at defects, developing analytical methods to predict fatigue failure is necessary for critical AM applications involving cyclic loadings. In this work a computational framework is presented where a generalized Paris equation and the Hartman-Schijve variant of NASGRO equation are used to predict the fatigue life of L-PBF Ti-6Al-4V and 17-4 PH stainless steel specimens. A variety of conditions resulting in different failure modes and defect sized were considered, including annealed and HIPed treatments as well as as-built and machined surface conditions. In contrast to the commonly assumed mode I crack growth in the literature, in this work the appropriate crack growth mode based on the observed failure mechanism is used for fatigue life predictions. By using Extreme Value Statistics (EVS) and equivalent defect size based on Murakami's method for approximation of the initial defect size, and appropriate crack growth equation and properties, good correlations between experimental results and prediction curves are demonstrated.

Automated summary

This work presents a computational framework for predicting the fatigue life of two metal specimens, considering different treatment conditions and defect sizes. Unlike the commonly assumed crack growth mode in the literature, the appropriate crack growth mode based on the observed failure mechanism is used for fatigue life predictions. Good correlations between experimental results and prediction curves are demonstrated by using Extreme Value Statistics and equivalent defect size based on Murakami's method for approximation of the initial defect size.