Machine learning-based predictions of fatigue life for multi-principal element alloys

Compared to conventional alloys, multi-principal element alloys (MPEAs) offer attractive and tunable fatigue properties. With the advancement of machine learning (ML) in material science, it is perceived as an effective tool for predicting materials fatigue life. Therefore, in this work, we aim to predict the room temperature fatigue lives of two classes of MPEAs, the single-phase CoaCrbFecMndNie system and the multi-phase AlfCogCrhFeiMnjNik system, using four different ML algorithms. Popular ML algorithms viz., Random Forest (RF), Support Vector Machine (SVM), and boosting algorithms such as GBOOST and XGBOOST were employed. Three pairs of test and training datasets were utilized to generalize the employed ML models. Input variables selection procedure was also carried out for possible improvement of algorithms' performance. SVM and boosting algorithms predicted fatigue lives of both classes of MPEAs within the band of the factor of two. Overall, all algorithms performed reasonably well, despite several hindering factors, such as the data's inherent scatter and limited datasets, which were acquired using different testing standards.