A novel multiaxial fatigue life prediction model based on the critical plane theory and machine-learning method

Abstract

In order to characterize the fatigue failure and damage mechanism under complex multiaxial loads, several multiaxial semi-empirical fatigue models, such as Fatemi-Socie (FS), Smith-Watson-Topper (SWT) and Wang-Brown (WB) models, were proposed to explain the relationship between fatigue life and stress/strain based on experimental analysis or observation. Although the semi-empirical model is widely used in practice because of its simplicity, but it is difficult to uniformly model the mean stress effect of a wide range of materials and loading conditions. To address this issue, a multiaxial fatigue life prediction model based on critical plane theory and machine learning is proposed in this work. Through the multi-layer stacking mechanism, the model comprehensively utilizes domain knowledge and original data information, and integrates the advantages of different models in capturing data and utilizing features. The experimental results showed that the proposed model achieves stable and highly accurate fatigue life prediction of the GH4169, wrought Ti-6Al-4V and TC4 materials with complex working conditions.

Keywords

Multiaxial fatigue damage fatigue life prediction machine learning critical plane stacked model

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