Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L

In aerospace engineering, many additive manufacturing (AM) metal parts subject to fatigue loadings, resulting in their fatigue failure. Therefore, it is essential to develop an advanced approach for fatigue issues. Although some theoretical methods are used for fatigue analysis of AM metal parts, their implementations are time-consuming. Furthermore, these methods cannot directly consider the effects of AM parameters. In this study, a platform is developed for a data-driven analysis of continuum damage mechanics (CDM)-based fatigue life prediction of AM stainless steel (SS) 316L, in which the effects of AM process parameters (including laser power P, scan speed v, hatch space h, powder layer thickness t) are considered. Here, three typical ML models: an artificial neural network (ANN), a random forest (RF), and a support vector machine (SVM), are trained effectively by a database produced by the CDM technique, and then further comparisons are made between the predicted results and published experimental data to verify the proposed platform. Finally, detailed parametric studies using the ML models are conducted to investigate some of the significant characteristics.