机制(生物学)
材料科学
工作(物理)
非线性系统
结构工程
计算机科学
人工智能
机械工程
工程类
物理
量子力学
作者
Jianbo Yang,Guozheng Kang,Qianhua Kan
标识
DOI:10.1016/j.ijfatigue.2022.106851
摘要
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.
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