Microhardness Prediction Model of Peened Parts Based on GA-BP Neural Network

被引：0

作者：

Shi M. ^{[1
]}

Wang Z. ^{[1
,2
]}

Gan J. ^{[3
]}

Yang Y. ^{[1
,2
]}

Wang X.-L. ^{[1
,2
]}

Ren X.-D. ^{[1
]}

Shen J.-G. ^{[1
]}

Qiu B. ^{[4
]}

机构：

[1] School of Automotive Engineering, b. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan

[2] Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan

[3] School of Transportation, Wuhan University of Technology, Wuhan

[4] China Automotive Engineering Research Institute Co., Ltd, Chongqing

来源：

Surface Technology | 2022年 / 51卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Genetic algorithm; Neural network; Prediction model; Shot peening; Surface microhardness;

D O I：

10.16490/j.cnki.issn.1001-3660.2022.01.036

中图分类号：

学科分类号：

摘要：

The work aims to establish a mathematical model that can accurately predict the surface microhardness of peened parts under different shot peening parameters. Taking 42CrMo steel as the research object, the shot peening experiment plan was designed by orthogonal experiment method and the point-by-point measurement method was used to measure the microhardness in the depth of 0~320 μm. The BP neural network was used to establish the surface microhardness prediction model of 42CrMo steel after shot peening. Meanwhile, genetic algorithm (GA) was used to optimize the structure of BP neural network, and the surface microhardness prediction model of 42CrMo steel after shot peening based on GA-BP neural network was established. Velocity, diameter, coverage and depth from surface were set as the input parameters, and the surface microhardness was set as the output parameter in both two models. The experimental data was divided into two parts, where the training set was used for the training of the two models, the correlation coefficient R of BP neural network model and GA-BP neural network model was about 0.97, and the training effect of the two models was good. By comparing the predicted value of two models and the experimental value of 20 groups of test set, it was found that the maximum and average relative errors between the predicted value of the BP neural network model and the experimental value were 3.5% and 1.1%, respectively. The maximum and average relative errors between the predicted value of the GA-BP neural network model and the experimental value were only 2.9% and 0.7%, respectively. The GA-BP neural network model had higher prediction accuracy and stability. The BP neural network optimized by genetic algorithm (GA-BP) is more suitable for establishing the prediction model of the surface microhardness of peened parts, which can provide some guidance for the industrial application. © 2022, Chongqing Wujiu Periodicals Press. All rights reserved.

引用

页码：332 / 338and357

共 20 条

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