An ML-Based Approach for HCF Life Prediction of Additively Manufactured AlSi10Mg Considering the Effects of Powder Size and Fatigue Damage

Abstract

As a popular technique, additive manufacturing (AM) has garnered extensive utilization in various engineering domains. Given that numerous AM metal components are exposed to fatigue loads, it is of significant importance to investigate the life prediction methodology. This study aims to investigate the high-cycle fatigue (HCF) behavior of AM AlSi10Mg, taking into account the influences of powder size and fatigue damage, and a novel ML-based approach for life prediction is presented. First, the damage-coupled constitutive model and fatigue damage model are derived, and the Particle Swarm Optimization method is employed for the material parameters’ calibration of M AlSi10Mg. Second, the numerical implementation of theoretical models is carried out via the development of a user-defined material subroutine. The predicted fatigue lives of AM AlSi10Mg with varying powder sizes fall within the triple error band, which verifies the numerical method and the calibrated material parameters. After that, the machine learning approach for HCF life prediction is presented, and the Random Forest (RF) and K-Nearest Neighbor (KNN) models are employed to predict the fatigue lives of AM AlSi10Mg. The RF model achieves a smaller MSE and a larger R2 value compared to the KNN model, signifying its superior performance in predicting the overall behavior of AM AlSi10Mg. Under the same maximum stress, a decrease in the stress ratio from 0.5 to −1 leads to a reduction in fatigue life for both powder sizes. As the powder size decreases, the rate of damage evolution accelerates, leading to shorter fatigue life.

Keywords

• machine learning
• high-cycle fatigue
• additive manufacturing
• fatigue life
• damage model