K9玻璃磁流变抛光材料去除效率的动态预测与工艺优化  

Dynamic Prediction and Optimization of Material Removal Rate for K9 Glass Magnetorheological Polishing

作　　者：卢明明[1] 刘宇强 林洁琼[1] 杨亚坤 孙少毅 LU Mingming;LIU Yuqiang;LIN Jieqiong;YANG Yakun;SUN Shaoyi(School of Mechanical and Electrical Engineering,Changchun University of Technology,Changchun 130012,China;Research and Development Department,Suzhou Maizhinuo Intelligent Equipment Technology Co.,Ltd.,Kunshan 215300,Jiangsu,China)

机构地区：[1]长春工业大学机电工程学院,长春130012 [2]苏州迈智诺智能装备科技有限公司研发部,江苏昆山215300

出　　处：《机械科学与技术》2025年第1期59-66,共8页Mechanical Science and Technology for Aerospace Engineering

基　　金：吉林省科技厅重点研发项目(20240302037GX);吉林省教育厅科研项目(JJKH20230751KJ);吉林省高性能制造与检测国际合作重点实验室(20220502003GH)。

摘　　要：该研究旨在精确预测磁流变抛光K9玻璃加工过程中的材料去除率,并找到最佳工艺参数组合。采用了响应面法(RSM)与粒子群优化算法(PSO)相结合的方法建立了材料去除率预测模型,并进行了最优工艺参数的搜索。首先,利用响应面法构建了动态预测模型,将工件转速、偏摆速度和工作间隙作为输入,K9玻璃的材料去除率作为输出,并研究了工艺参数与材料去除率之间的交互影响。随后,利用粒子群优化算法进行全局寻优,并通过实验验证了最优工艺参数。结果表明:构建的动态预测模型具有高精度,相关系数R^(2)=0.9887,调整决定系数R_(adj)^(2)=0.9388。各工艺参数与材料去除率均存在交互作用,但工件转速与工作间隙的交互作用影响最小。粒子群优化算法寻优得到的最佳工艺参数组合为:工件转速600 r/min、偏摆速度102 mm/min、工作间隙2.5 mm。预测的K9玻璃的材料去除率为0.739μm/min,实际为0.719μm/min,误差仅为2.8%。该研究为磁流变抛光K9玻璃的材料去除效率动态预测及工艺参数优化提供了一定的指导意义。The aim of this study is to accurately predict the material removal rate(MRR)during the processing of magnetorheological polishing(MRP)K9 glass and find the best combination of process parameters.A combination of response surface methodology(RSM)and particle swarm optimization(PSO)algorithm was used to develop a material removal rate prediction model and search for optimal process parameters.Firstly,a dynamic prediction model was constructed using the response surface method,where the workpiece speed,yaw speed and working gap were taken as inputs and the MRR of K9 glass was taken as outputs,and the interaction between the process parameters and the MRR was investigated.Subsequently,we performed global optimization using PSO and experimentally verified the optimal process parameters.The results show that the constructed dynamic prediction model has high accuracy with a correlation coefficient R^(2)=0.9887 and an adjusted coefficient of determination R_(adj)^(2)=0.9388.There was an interaction between each process parameter and MRR,but the interaction between workpiece speed and working gap had the least effect.The optimum combination of process parameters obtained by PSO optimization was 600 r/min for workpiece speed,102 mm/min for yaw speed and 2.5 mm for working gap.The predicted MRR of 0.739μm/min for K9 glass was actually 0.719μm/min,an error of only 2.8%.This study provides some guidance for the dynamic prediction of MRR and optimization of process parameters for MRP of K9 glass.

关 键 词：K9 磁流变抛光 响应曲面法 粒子群优化算法 材料去除率 动态预测 

分 类 号：TG156[金属学及工艺—热处理]