Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach

•ML framework for fatigue life prediction of AM Ti-6Al-4V samples is proposed.•ANN, RFR and SVR models are used for fatigue life prediction.•Spearman’s rank correlation test is applied to identify insensitive features.•The LOOCV technique is employed in the optimization of the ML models. In this work, a framework based on the machine learning (ML) approach and Spearman’s rank correlation analysis is introduced as an effective instrument to solve the influence of defects detected by micro-computed tomography (μCT) method, and stress amplitude on the fatigue life performance of AM Ti-6Al-4V. Artificial neural network (ANN), random forest regressor (RFR) and support vector regressor (SVR) models are implemented and optimized. The optimization is performed on training set by tuning the hyperparameters and parameters using the leave-one-out cross validation (LOOCV) technique. The results present comparison between predicted and experimental results and validate the proposed framework.