Life prediction for LY12CZ notched plate based on the continuum damage mechanics and the genetic algorithm and radial basis function method

In this paper, a new method based on the Continuum Damage Mechanics (CDM) and the Genetic Algorithm and Radial Basis Function neural network method (GARBF) is proposed to predict fatigue life of LY12CZ notched plate. Firstly, the multiaxial fatigue damage evolution equation is derived, and the fatigue life of the notched specimen is predicted based on the CDM method. Secondly, the RBF method is introduced to modify the relative deviation between the theoretical result and actual life. According to the drawbacks of the RBF method, the GA is adopted to optimize network parameters to effectively improve the model quality and reduce the training error. Then, the verification test indicates that the combined method of CDM and GARBF is able to reduce the average relative error of the results of fatigue life prediction to about 7%, which shows that the new method to predict the fatigue life is more reliable. At last, compared with the predicted results of the traditional Back Propagation (BP) neural network, the GARBF model proposed in this paper has a better optimization effect and the result is more stable. This research provides a feasible way to predict the fatigue lives of the notched plate based on the CDM and GARBF method.