Knowledge assisted machine learning to clarify pore influence on fatigue life of forging/additive hybrid manufactured Ti-17 alloy

Abstract

Forging/additive hybrid manufactured Ti alloy parts suffer from relatively low fatigue life due to the existence of metallurgical defects in the transition zone, which also brings difficulty to fatigue life modeling. In this work, the synergistic effect of pore size and location on the rotating-bending fatigue life of hybrid manufactured Ti-5Al-2Sn-2Zr-4Mo-4Cr (Ti-17) samples was systematically investigated with the combination of machine learning approaches and physical knowledge. A machine learning framework with a back propagation neural network and generative adversarial network (GAN) was constructed and employed on sparse and limited datasets. A general and interpretable model was obtained with a high level of 90% confidence. In general, the fatigue life of hybrid manufactured Ti-17 alloys decreases with pore size and increases with its distance to surface. Specifically, critical sizes were obtained for near-surface and in-depth pores that have negligible influence on fatigue life of hybrid manufactured samples with respect to pore-free samples. The present work thus provides a systematic platform for the evaluation of the fatigue performance of hybrid manufactured titanium alloys.