Fatigue crack growth prediction method based on machine learning model correction. (15th December 2022) - Fang, Xin; Liu, Guijie; Wang, Honghui; Xie, Yingchun; Tian, Xiaojie; Leng, Dingxin; Mu, Weilei; Ma, Penglei; Li, Gongbo
Abstract: At present, ML has become an effective method to solve the prediction problem of fatigue crack growth. To reduce the inaccurate prediction caused by uncertain factors in crack growth, this paper proposes a fatigue crack growth prediction method based on the ML model correction. This method improves the accuracy of crack growth prediction by using real crack data to correct the ML model. In the research process, the prediction performance of the three ML methods is compared, and the CGR-ML model for crack growth is established. Subsequently, dynamic correction strategy for the CGR-ML model is proposed while selecting crack detection points by using the nonlinear crack length selection method. Finally, the effectiveness of the method is verified by the central crack growth and the crack growth experiment under mixed-mode multi-step loading. It can be seen from the comparison with the previously proposed fatigue crack growth prediction method based on the theoretical model correction that the method proposed in this paper can achieve a better prediction effect. Highlights: The performance of three machine learning methods in predicting crack growth rate is compared. A dynamic correction strategy for crack growth rate - machine learning model is proposed. A fatigue crack growth prediction framework based on ML model correction is proposed. The proposed method can achieve the dynamic and accurate fatigue crack growth prediction.