# [1]王 栋,张志鹏,赵 睿,等.基于遗传算法的磨削力模型系数优化及验证[J].郑州大学学报(工学版),2024,45(01):21-28.[doi:10.13705/j.issn.1671-6833.2023.04.010] 　WANG Dong,ZHANG Zhipeng,ZHAO Rui,et al.Coefficient Optimization of Grinding Force Model Based on Genetic Algorithm[J].Journal of Zhengzhou University (Engineering Science),2024,45(01):21-28.[doi:10.13705/j.issn.1671-6833.2023.04.010] 点击复制 基于遗传算法的磨削力模型系数优化及验证() 分享到： var jiathis_config = { data_track_clickback: true };

45

2024年01期

21-28

2024-01-19

## 文章信息/Info

Title:
Coefficient Optimization of Grinding Force Model Based on Genetic Algorithm

Author(s):
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:
DOI:
10.13705/j.issn.1671-6833.2023.04.010

A

Abstract:
when solving problems in the grinding force model, most of the methods of segmental calculation or column equations were used to calculate each coefficient directly, which not only demanded a large amount of calculation but also could not guarantee its accuracy. In addition the traditional regression model was easy to fall into local optimal, difficult to describe the nonlinear relationship. Therefore, the genetic algorithm was introduced into the parameter optimization of the nonlinear fitting function, and the coefficient optimization method of the theoretical model of grinding force was studied based on the existing model data such as the model of cylindrical transverse grinding, the model of plane grinding and the model of cylindrical longitudinal grinding. Correlation analysis results showed that the predicted accuracy of grinding force of the three models was increased by 14. 69% -42. 54%. The average error of normal grinding force predicted by the three models was 5. 9%, 9. 13% and 3. 23%, respectively. The mean error of tangential force was 6. 78%, 8. 36% and 3. 69%, respectively. Through comparison, it could be concluded that the optimized model had a better fitting degree, and the prediction accuracy of the model was significantly improved. The nonlinear fitting function GA-LSQ algorithm optimized by genetic algorithm was more suitable for solving grinding force model and could provide reference for predicting grinding force and parameter optimization in actual production.

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