A machine learning based approach with an augmented dataset for fatigue life prediction of additively manufactured Ti-6Al-4V samples

Abstract

The article deals with the prediction of fatigue life using a machine learning (ML) approach. The original dataset is based on the parameters of defects obtained by micro-computed tomography (µ-CT) prior to fatigue tests, stress level and the fatigue life of additively manufactured (AM) Ti-6Al-4V samples. As the original dataset is considered too small to train a comprehensive ML model, the study proposed a novel approach for dataset augmentation. Dataset augmentation is done using inverse transform sampling and multivariate radial basis function (RBF) interpolation with various values of the smoothing parameter (λ). Finally, ML model accuracy is improved up to 0.953 of coefficient of determination (R2).