基于改进粒子群算法的刀具瞬时铣削力预测研究  

Tool Instantaneous Milling Force Prediction Based on Improved Particle Swarm Optimization Algorithm

作　　者：鲁旭祥 王宸 李宗良[3] 刘超 张秀峰 Lu Xuxiang;Wang Chen;Li Zongliang;Liu Chao;Zhang Xiufeng(College of Mechanical Engineering,Hubei University of Automotive Technology,Shiyan,Hubei 442002,China;不详)

机构地区：[1]湖北汽车工业学院机械工程学院,湖北省十堰市442002 [2]上海大学上海市智能制造与机器人重点实验室 [3]东风越野车有限公司

出　　处：《工具技术》2022年第11期88-93,共6页Tool Engineering

基　　金：国家自然科学基金(51475150);国家科技重大专项(2018ZX04027001);教育部人文社科项目(20YJCZH150);汽车动力传动与电子控制湖北省重点实验室基金(ZDK1201703);湖北汽车工业学院博士基金(BK201905)。

摘　　要：针对刀具微元铣削力模型预测精度低的问题,建立了基于刀具径向跳动的瞬时铣削力模型,采用改进惯性权重的粒子群算法(IWPSO)对模型进行系数求解。通过改进粒子群算法,避免算法过早收敛而陷入局部最优,提高了算法的速度和精度,降低了模型系数的求解难度,从而减小模型预测铣削力的误差。通过与线性拟合方法求解的铣削力系数对比,在0~0.1s内铣削力预测波形图的波谷与实际铣削力波形图误差较小(5%以内),验证了此模型的精度更高。采用不同铣削参数进行实验,验证了铣削力预测模型预测铣削力的准确性,对实际的铣削加工有着重要意义。To address the problem of low prediction accuracy of the tool micro-element milling force model, the model of the instantaneous milling force based on the radial runout of the tool is firstly established, and then an improved particle swarm optimization algorithm(IWPSO) with inertia weights is used to address the coefficients of the model.With the help of improved the particle swarm optimization algorithm, the premature convergence of the algorithm falling into a local optimum is avoided, the speed and accuracy of the algorithm is improved, the difficulty of solving the model coefficients is reduced, and thus the error of the model predicting the milling force is decreased.By comparing with the linear fitting method to solve the milling force coefficients, the error between the trough of the milling force prediction waveform and the actual milling force waveform within 0~0.1 s is smaller, less than 5%,which verifies that this the accuracy of the model is higher.Finally, by experimenting with different milling parameters, the accuracy of the milling force prediction model and predicted milling force is verified, which is of great significance to the actual milling process.

关 键 词：铣削力模型 径向跳动 粒子群算法 系数求解 

分 类 号：TG54[金属学及工艺—金属切削加工及机床] TH164[机械工程—机械制造及自动化]