基于支持向量回归机的磨削力预测  

Prediction of Grinding Force Based on Support Vector Regression

作　　者：单晓敏[1] 李峰[2] 吴晓强[1] 黄云战[3] 

机构地区：[1]内蒙古民族大学机械工程学院,内蒙古通辽028000 [2]长春职业技术学院,吉林长春130033 [3]云南农业大学工程技术学院,云南昆明650201

出　　处：《实验室研究与探索》2016年第10期24-27,共4页Research and Exploration In Laboratory

基　　金：国家自然科学基金项目(6144041);国家科技部农业科技成果转化项目(2011GB2F300003)

摘　　要：针对神经网络方法在磨削力预测方面存在的网络结构不好确定和样本需求量大等不足,提出了一种新的基于支持向量回归机的磨削力智能预测方法。介绍了支持向量回归机的基本原理,分析了影响磨削力的主要因素,选用砂轮速度、工件速度和磨削深度作为输入参数,建立了基于支持向量回归机的磨削力预测模型。仿真结果表明,所建立的预测模型是合理有效的,与BP神经网络预测方法相比,预测的结果准确性更高。There is a complicated nonlinear relationship between the grinding force and grinding parameters,and the effective prediction of the grinding force is of great significance for monitoring the wear of grinding wheel and grinding quality. In view of the shortage of the network structure of neural network method in the prediction of grinding force and the large sample required,a new grinding force intelligent prediction method based on support vector regression is proposed. The basic principle of support vector regression machine is introduced,and analysis the main factors affecting the grinding force,selection of wheel speed,work piece speed and grinding depth three indicators as input parameters,are established based on support vector regression machine grinding force prediction model. Experimental results show that the prediction model is reasonable and effective,and the results are more accurate than the BP neural network prediction method.

关 键 词：磨削力 预测 支持向量机 支持向量回归机 

分 类 号：TH16[机械工程—机械制造及自动化]