Abstract
The analysis of the residual life of high temperature low cycle fatigue of 30CrMnSiA steel plays important roles in improving security and avoiding accidents. In this paper, the RBF neural network method is used to predict the residual life of high temperature low cycle fatigue of 30CrMnSiA steel base on data from the thermo-mechanical fatigue test. The feasibility of the method is proved by a practice example, and the learning results are in good agreement with the experimental data.
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