细长轴磨削变形的变速优化智能预测控制研究  

Study on Variable Velocity Optimal Intelligent Prediction Control for Slender Workpiece Deformation in Grinding Process

作　　者：丁宁[1] 王龙山[2] 李国发[2] 陈向伟[2] 

机构地区：[1]长春大学,长春130022 [2]吉林大学,长春130025

出　　处：《中国机械工程》2006年第1期21-24,28,共5页China Mechanical Engineering

基　　金：吉林省科技发展计划资助项目(20020632)

摘　　要：在建立细长轴磨削过程中工件弹性变形数学模型的基础上,打破了传统的恒速控制方法,提出了一种控制细长轴磨削弹性变形的变速优化适应控制策略:根据磨削系统沿工件轴向各点刚度的不同,通过不断改变工件转速和纵向进给速度,控制法向磨削力的变化,进而控制工件的弹性变形;同时,由一个神经网络预测系统和一个模糊控制系统实时控制加工过程中的磨削深度,进一步控制加工中由于砂轮磨损而引起的细长轴形状误差。仿真和实验结果表明:变速优化适应控制策略和模糊神经网络预测控制方法是可行的,可极大地提高磨削生产率,减小细长轴的形状误差。Breaking the traditional constant velocity control method,a variable velocity optimal adaptive control strategy was proposed based on the mathematical model of slender workpiece flexible deformation in grinding process. The optimal control strategy put forward here is： according to the difference in stiffness along the workpiece axis, it is made possible by changing the workpiece rotational velocity and table feed velocity to control the normal force, and then control the workpiece flexible deformation. At the same time, the grinding depth was controlled in real time by a neural network prediction system and a fuzzy control system,which can control the workpiece deformation caused by grinding wheel wear. Simulation and experimental results validate and verify that the proposed control method is feasible, and can greatly improve grinding efficiency and reduce the shape errors.

关 键 词：细长轴 变速 优化适应控制 神经网络 预测 模糊控制 

分 类 号：TG580.6[金属学及工艺—金属切削加工及机床] TH166[机械工程—机械制造及自动化]