Prediction of fatigue crack growth using convolutional neural network (1st Report, Prediction for a single crack with angle)

Abstract

This paper presents a method for predicting the crack growth shape and the number of cycles of a two-dimensional fatigue crack under cyclic loading using a convolutional neural network. All of data sets for train are generated by s-version FEM for fatigue crack propagation analysis. The crack propagation simulations were simulated with different slant angles. Crack tip coordinates, crack growth vectors, and numbers of cycles are prepared as a set of train data for one prediction step, which is determined by the minimum mesh size of the crack tip in the s-FEM simulation. Data augmentation technique, which adds a slight noise to input data, is introduced as regularization in this work. We'd like to evaluate the effectiveness of the data augmentation. Additionally, the interpolation ability and extrapolation ability of the prediction model are evaluated. The crack growth shapes and the number of cycles in the prediction step can be predicted within 6%, 11.1% difference with the reference.

Keywords

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