On the development of error-trained BP-ANN technique with CDM model for the HCF life prediction of aluminum alloy

A novel method is presented for the high cycle fatigue (HCF) life prediction of aluminum alloys, and the error trained back propagation artificial neural network (BP-ANN) technique with the continuum damage mechanics (CDM) model is developed. First, the experimental data and numerically computed fatigue lives by the CDM model are combined to constitute a database, and the relative errors are taken as the training targets for the ANN model. Second, the relationship is established between the relative errors and external parameters (such as fatigue loads, stress concentration factors and so on). The predicted errors are then used as gains to adjust the numerical results, as the final predicted fatigue lives. At last, the HCF lives of the LC4 specimens are predicted by three different methods. It is found that there exists a relatively large error in the predicted results by the CDM finite element method. For the ANN model trained only with the experimental data, the accuracy of the predicted fatigue lives are not as good as the proposed technique, which also could maintain a better stability.

Automated summary

A novel method combining the error trained back propagation artificial neural network (BP-ANN) technique with the continuum damage mechanics (CDM) model is proposed for predicting the high cycle fatigue (HCF) life of aluminum alloys. Experimental data and numerically computed fatigue lives are used to train the ANN model, and the predicted errors are used to adjust the numerical results for final fatigue life prediction. The proposed technique shows better accuracy and stability compared to other methods.