Curved fatigue crack growth prediction under variable amplitude loading by artificial neural network

By Bowen Wang, Liyang Xie, Jiaxin Song, Bingfeng Zhao, Chong Li, Zhiqiang Zhao. 2021

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Abstract The focus of this study is to predict curved crack FCG failure under variable amplitude load effectively and accurately. Based on the artificial neural network (ANN) and FCG path/life prediction models, a numerical calculation method is designed. The proposed method considers the underlying physical mechanism of cracked structure, and only a relatively small amount of finite element calculations are required to predict FCG problems with different initial conditions. Furthermore, it can be found that the geometric parameters of hole and crack have an effect on FCG. Finally, compared with the experimental and simulation results of different examples, the effectiveness of the new method is verified.

Subfields: Civil and Structural Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/bdfdc9be924bd0539d641f60e43986a5a57fea64