Abstract
The purpose of this paper is to analyse fatigue crack growth under mixed mode conditions using artificial neural networks. Mixed mode conditions were achieved using a four-point bending specimen, with different notch positions, and a modified compact tension specimen, with one hole using different diameters and centre coordinates. Finite element analysis simulations, using the vector crack tip displacement propagation criterion, were used to train the artificial neural networks. Once fully trained, the artificial neural networks were able to predict fatigue crack growth paths and lives under mixed mode conditions, showing good agreement with both experimental and numerical examples.
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