Prediction model of low cycle fatigue life of 304 stainless steel based on genetic algorithm optimized BP neural network

The low cycle fatigue life of 304 stainless steel is an essential basis for safety assessment. Usually, there is a complex nonlinear relationship between fatigue life and influencing factors, which is difficult to be predicted by traditional fatigue life models. Based on this, the BP algorithm and g...

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Bibliographic Details
Published in:Materials research express 2022-07, Vol.9 (7), p.76511
Main Authors: Cao, Mengjie, Duan, Hongyan, He, Hong, Liu, Yang, Yue, Shunqiang, Zhang, Zengwang, Zhao, Yingjian
Format: Article
Language:English
Subjects:
304 stainless steel
Austenitic stainless steels
Back propagation networks
BP neural network
Correlation coefficients
Fatigue life
Fatigue tests
genetic algorithm
Genetic algorithms
Life prediction
Low cycle fatigue
Optimization
Prediction models
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Summary:The low cycle fatigue life of 304 stainless steel is an essential basis for safety assessment. Usually, there is a complex nonlinear relationship between fatigue life and influencing factors, which is difficult to be predicted by traditional fatigue life models. Based on this, the BP algorithm and genetic optimization algorithm (GA) for the fatigue life prediction problem of 304 stainless steel is proposed. Based on the existing large amount of test data, the fatigue life of 304 stainless steel material is predicted by using BP and GA-BP learning models. The results show that the GA-BP prediction model is more flexible, the correlation coefficient R reaches 0.98158, the prediction data are within the 2 times error limit and closer to the ideal line, and the model prediction is better.
ISSN:2053-1591
2053-1591
DOI:10.1088/2053-1591/ac7cc0