A microstructure sensitive machine learning-based approach for predicting fatigue life of additively manufactured parts

Accurate fatigue life prediction of additive manufactured parts is critical for the reliability and safety assessment of the designs made for aerospace applications. The fatigue life depends on the cyclic stress experienced due to loads in operation, surface roughness, internal microstructure, and defects in the parts. The microstructure of a material contains signatures of the manufacturing process and post-processing experienced by the part. Incorporating microstructure information in fatigue life prediction is difficult using analytical and empirical relations. A data–driven machine learning framework can be used to model complex phenomena without solving the detailed underlying physics. Manual selection of important features from microstructure may not capture all the properties that affect fatigue. In this work, the fatigue data of Ti-6Al-4V alloy is collected from several sources and machine learning models are trained using surface roughness, stress cycles and microstructure images. The effect of utilizing microstructure images and their 2-point statistics data with convolutional neural networks and Gaussian process regression for prediction of fatigue life are demonstrated. Various methods of image processing, data preparation, and modeling techniques are studied and outcomes are discussed.