Prediction of fatigue crack growth using convolutional neural network (2nd Report, Prediction of crack propagation on different levels)

Abstract

In this paper, the prediction of crack propagation with two cracks using machine learning is described. The analysis results of crack propagation by s-version FEM (s-FEM), which combines the automatic mesh generation technique, are used for generation of training and validation datasets. Plural crack propagation with the different vertical distance between the two cracks as a variable are analyzed. The analysis cases are divided into training and validation datasets. In training process, the input parameters are the coordinates of 4 crack tip, the output data are crack propagation vectors, the number of cycles for crack propagation of 0.25 mm. Initial crack configurations should be specified. After the specification, the predictor iteratively predicts crack propagation direction and the number of loading cycles. A prediction accuracy depends on the training datasets, which contains 0.25 mm length of each crack propagation in this study. To improve prediction accuracy, the data augmentation is effectively applied. In case of plural crack interaction, when the crack tips close each other, the accuracy gets worse and worse. Reducing datasets which satisfy the crack coalescence condition, it is shown that the prediction accuracy is improved. Even if training datasets are not enough number for accurate prediction, it is shown that the prediction accuracy is improved by the data augmentation.

Keywords

• fatigue crack growth
• crack growth prediction
• interaction effect
• s-version fem
• convolutional neural network
• data augmentation
• deep learning