基于最小二乘支持向量机的外圆磨削表面粗糙度预测系统  

Prediction system of surface roughness based on LS-SVM in cylindrical longitudinal grinding

作　　者：黄吉东[1,2] 王龙山[1] 李国发[1] 张秀芝[1] 王家忠[3] 

机构地区：[1]吉林大学机械科学与工程学院,吉林长春130020 [2]北华大学机械工程学院,吉林吉林132021 [3]河北农业大学机电工程学院,河北保定071001

出　　处：《光学精密工程》2010年第11期2407-2412,共6页Optics and Precision Engineering

基　　金：吉林省科技发展计划资助项目(No.20060534)

摘　　要：为解决磨削加工中影响因素多,难以实现自动化加工的困难,对磨削系统的表面粗糙度预测系统进行了研究。在分析目前常用预测方法的基础上,建立了基于最小二乘支持向量机的外圆纵向磨削表面粗糙度预测模型。该模型采用等式约束,把原来求解一个二次规划问题转化成求解一个线性方程组,方法简单且有效。比较实验显示,该方法响应时间快、测量精度高,测量精度误差比BP神经网络预测方法小4%,比进化神经网络(BP+GA)预测方法小1.3%,所提供的预测方法可以实现对工件表面粗糙度的在线预测。将其应用于外圆纵向磨削智能系统中,实时计算预测值与给定粗糙度的差值,引导磨削专家系统修正磨削参数,实现智能控制,取得了较好的效果。A prediction model of surface roughness based on the Least Square Support Vector Machine（LSSVM） in cylindrical longitudinal grinding is proposed. By converting the inequality constraints into equality constraints, the model transformes solving the SVM from a Quadratic Programming （QP） problem to a group of linear equations, which simplifies the learning process and improves the calculating efficiency. Experimental results indicate that the construction speed of the prediction model based on LS-SVM is more faster , and the measurement error（MSE） is less 4% and 1.3% than those of the BP neutrol algorithm and BP＋GA algorithm,respectively. The method has been used in a intelligent system for cylindrical longitudinal grinding to predict the surface roughness of a workpiece in real time. By calculating the differences of predicating values and giving values and by directing the correct of grinding parameters,it completes a closed loop and intelligent control and obtains good grinding results.

关 键 词：外圆纵向磨削 最小二乘支持向量机 表面粗糙度 

分 类 号：TG580.6[金属学及工艺—金属切削加工及机床] TP391.4[自动化与计算机技术—计算机应用技术]