[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,(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,(01):21-28.[doi:10.13705/j.issn.1671-6833.2023.04.010]
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基于遗传算法的磨削力模型系数优化及验证()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
期数:
2024年01期
页码:
21-28
栏目:
出版日期:
2024-01-19

文章信息/Info

Title:
Coefficient Optimization of Grinding Force Model Based on Genetic Algorithm
作者:
王 栋 张志鹏 赵 睿 张君宇 乔瑞勇 孙少铮
郑州大学 机械与动力工程学院,河南 郑州 450001
Author(s):
WANG Dong ZHANG Zhipeng ZHAO Rui ZHANG Junyu QIAO Ruiyong SUN Shaozheng
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
磨削力模型 外圆磨削 平面磨削 经验公式 模型系数优化 模型预测 遗传算法 非线性优化函数
Keywords:
grinding force model cylindrical grinding surface grinding empirical formula model coefficient optimization model prediction genetic algorithm nonlinear optimization function
DOI:
10.13705/j.issn.1671-6833.2023.04.010
文献标志码:
A
摘要:
在磨削力模型求解问题中,目前大多使用分段计算法或列方程组直接计算各个待求系数,不仅计算量大 且其精度也无法保证。 另外,传统的回归模型容易陷入局部最优,难以描述非线性关系。 为此,将遗传算法引入到 非线性优化函数参数优化中,基于外圆横向磨削力模型、平面磨削力模型、外圆纵向磨削力模型等现有的模型数 据,开展磨削力理论模型的系数优化方法研究。 相关性分析结果表明:通过计算得到的 3 种模型磨削力的预测精 度提高了 14. 69% ~ 42. 54%,且 3 种模型所预测的法向磨削力的平均误差分别为 5. 9%、9. 13%、3. 23%,切向力平均 误差分别为 6. 78%、8. 36%、3. 69%。 经对比知,优化后的模型拟合度较好,模型预测精度显著提高。 遗传算法优化 后的非线性优化函数 GA-LSQ 算法更适合磨削力模型的求解,可对磨削力的预测及实际加工生产中的参数优化提 供参考。
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.
更新日期/Last Update: 2024-01-23