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

• Published in: International journal of fatigue 2021-01, Vol.142, p.105886, Article 105886
• Main Authors: Wang, Bowen, Xie, Liyang, Song, Jiaxin, Zhao, Bingfeng, Li, Chong, Zhao, Zhiqiang
• Format: Article
• Language: English
• Subjects: Artificial neural network; Curved fatigue crack growth; Cyclic variable amplitude loading; Numerical simulation; Uncertainty of initial conditions
• ISSN: 0142-1123; 1879-3452
• DOI: 10.1016/j.ijfatigue.2020.105886

[Display omitted] •An efficient and accurate method for fatigue crack growth path and life prediction is proposed.•The effect of hole and the interaction between cracks are considered in the FCG path prediction.•The retardation effect and the change of crack tip stress state are considered in the FCG life prediction.•Validity of the new method is verified quantitatively with experimental and simulation results. 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.