Research on Prediction Accuracy of Flow Stress of 304 Stainless Steel Based on Artificial Neural Network Optimized by Improved Genetic Algorithm

被引：0

作者：

Ding J. ^{[1
]}

Gu Y. ^{[1
]}

Huang X. ^{[1
]}

Song K. ^{[1
]}

Lu S. ^{[1
]}

Wang L. ^{[2
]}

机构：

[1] College of Mechanical Engineering, Chongqing University of Technology, Chongqing

[2] School of Materials Science and Engineering, Hefei University of Technology, Hefei

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2022年 / 58卷 / 10期

关键词：

304 stainless steel; artificial neural network; flow stress; genetic algorithm;

D O I：

10.3901/JME.2022.10.078

中图分类号：

学科分类号：

摘要：

The development in the defense and military industry such as the aerospace and weaponry require the higher mechanical performance for the metallic materials under the elevated temperature coupled with the high strain rate conditions. In the light of the values for 304 stainless steel from the experimental measurements, a new artificial neural network(ANN) model is proposed, which is optimized by modifying the selection operator in the genetic algorithm to predict the values for the flow stress of metallic materials in the complicated service condition. The improved model of the flow stress prediction is established on the basis of new ANN method from the experiments for the strain range of 0.1-0.5, temperature range of 20-600 ℃ and strain rate range of 0.001-100 s−1. Taken the mean absolute error(MAE) and the determination coefficient(R2) as the criterions, the results calculated from the improved model are compared with those from the regression tree model(RR), linear regression model(LR) and the unimproved genetic neural network model(GNN). The MAE and R2 for the ANN optimized by improved genetic Algorithm model shows the minimum value of 21.91 and the maximum of 0.97, respectively, in comparison with RR, LR and GNN model, which indicates that it can accurately predict the flow stress of 304 stainless steel. © 2022 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.

引用

页码：78 / 86

页数：8

共 30 条

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