电子测量与仪器学报

王兴磊,杨赫然,孙兴伟,潘飞,刘寅.多头螺杆转子双砂带磨削表面粗糙度和材料去除率双目标优化[J].电子测量与仪器学报,2025,39(2):177-184

多头螺杆转子双砂带磨削表面粗糙度和材料去除率双目标优化

Optimization of surface roughness and material removal rate fordouble belt grinding of multihead screw rotors

DOI：

中文关键词: 双砂带磨削表面粗糙度材料去除率多目标指数分布优化器双目标优化

英文关键词:double abrasive belt grinding surface roughness material removal rate multi-objective exponential distribution optimizer dual-target optimization

基金项目:2024年辽宁省教育厅高等学校基本科研项目领军人才团队项目（LJ222410142011）资助

作者	单位
王兴磊	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
杨赫然	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
孙兴伟	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
潘飞	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
刘寅	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870

Author	Institution
Wang Xinglei	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Yang Heran	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Sun Xingwei	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Pan Fei	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Liu Yin	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China

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中文摘要:

为保证多头螺杆转子砂带磨削质量的同时提高磨削效率。采用双砂带磨削装置对多头螺杆转子进行磨削，设计正交试验，利用正交试验数据库建立鲸鱼优化算法-径向基函数（WOA-RBF）预测模型，以决定系数R2、均方根误差（RMSE）和平均绝对误差（MAE）评判预测模型准确性，结果均优于其对比模型。以WOA-RBF预测模型的输出表面粗糙度和材料去除率数值作为双目标优化模型的目标函数，建立基于多目标指数分布优化器（MOEDO）的双目标优化模型。模型求解得到帕累托最优解集，通过评价函数得到优化后的工艺参数数值以及表面粗糙度和材料去除率数值。以优化后的工艺参数进行多头螺杆转子双砂带磨削试验验证，磨削后螺杆转子的表面粗糙度和材料去除率分别为0.462 μm和7.78 mm3/s，试验结果与双目标优化结果的误差均在合理误差之内，验证了模型的准确性。双目标优化结果与正交试验中效果最好的一组试验结果进行对比，表面粗糙度升高了37.5%，但仍然符合工件的技术要求，而材料去除率提高了84.23%。表明提出的双目标优化模型可以实现保证表面质量的同时提高磨削效率，也可为其他加工工艺中，表面质量及材料去除率的决策优化提供借鉴。

英文摘要:

In order to ensure the grinding quality of the multi-head screw rotor belt grinding quality and improve the grinding efficiency at the same time. The multi-head screw rotor was ground by using a double abrasive belt grinding device, an orthogonal test was designed, and the WOA-RBF prediction model was established by using the orthogonal test database to judge the accuracy of the prediction model by the coefficient of determination R2, root mean square error (RMSE) and mean absolute error (MAE), and the results were better than the comparison models. The output surface roughness and material removal rate values of the WOA-RBF prediction model were used as the objective functions of the dual-objective optimization model, and a dual-objective optimization model based on the Multi-objective exponential distribution optimizer (MOEDO) was established. The model is solved to obtain the Pareto optimal solution set, and the optimized process parameter values, surface roughness and material removal rate values are obtained through the evaluation function. The surface roughness and material removal rate of the grinding screw rotor after grinding were 0.462 μm and 7.78 mm3/s, respectively, and the errors between the test results and the dual-objective optimization results were within a reasonable error, which verified the accuracy of the model. The results of the dual-objective optimization were compared with the results of the best set of orthogonal tests, and the surface roughness increased by 37.5%, but still met the technical requirements of the workpiece, while the material removal rate increased by 84.23%.The results show that the proposed dual-objective optimization model can improve the grinding efficiency while ensuring the surface quality, and can also provide a reference for the decision-making optimization of surface quality and material removal rate in other processing processes.

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