Feature Transfer Learning for Fatigue Life Prediction of Additive Manufactured Metals With Small Samples

Abstract

A feature transfer learning (FTL)-based model is proposed to address small-sample problems in fatigue life prediction of additively manufactured (AM) metals. Transfer component analysis (TCA) is studied for data alignment before model training. Correspondingly, two TCA improvement strategies are further considered to aggregate training data from distinct AM processing conditions. An experimental database consisting of 103 fatigue data is built for model evaluation. The results demonstrate that the proposed model outperforms conventional machine learning models and other transfer learning-based models in terms of accuracy and data demand, showing good applicability for AM fatigue life assessment.