The potency of defects on fatigue of additively manufactured metals

Given their preponderance and propensity to initiate fatigue cracks, understanding the effect of processing defects on fatigue life is a significant step towards the wider application of additively manufactured (AM) parts. Here a novel machine learning (ML) based approach has been developed to predict the fatigue life of laser powder bed fused AlSi10Mg alloy. The four most important parameters, treferred to here as the Wu-Withers parameters, were found to be the applied stress and the projected area, location and morphology of the critical defects. It was found that an Extreme Gradient Boosting model was able to predict the fatigue lives with high accuracy with the importance of these characteristics in limiting fatigue life ranked in the order given above. The model was able to predict the very different lives of samples tested parallel and perpendicular to the build direction in terms of these four W-W parameters indicating that microstructure was of minor importance. In particular the large projected area of the defects on the crack plane when testing parallel to the build direction was found to be primarily responsible for the shorter lives observed for this testing orientation. The fatigue lives were adequately predicted by the more general two variable (stress and projected area) Murakami model, and even more closely predicted by an empirical model (using essentially the same four W-W parameters) for which the ML model corroborated the empirical dependences.

Automated summary

A novel machine learning approach was developed to predict the fatigue life of laser powder bed fused AlSi10Mg alloy, with important parameters including applied stress and critical defect characteristics. The model accurately predicted fatigue lives, showing the significance of defect projected area in limiting fatigue life.