A novel deep learning approach of multiaxial fatigue life-prediction with a self-attention mechanism characterizing the effects of loading history and varying temperature

Abstract

A novel deep learning approach is established in this work to directly model the highly nonlinear mapping between the complex loading conditions (input) and the multiaxial fatigue life (output). An advanced deep learning mechanism, named as self-attention mechanism, is incorporated in this approach to characterize the effects of complex loading history and varying temperature on the fatigue life. Three typical examples are performed to verify the capability of proposed approach to achieve the mechanical and thermo-mechanical multiaxial fatigue life-predictions. The results demonstrate that both the effects of loading history and varying temperature on the multiaxial fatigue life are reasonably captured, and the predicted lives by the proposed approach are almost located within the scatter band of 1.5 times.