摘要
针对神经网络方法预测并联机床表面粗糙度存在的不足,提出了一种新的基于支持向量回归机的并联机床表面粗糙度预测方法。以某型并联研抛机床为例确定了表面粗糙度预测模型的输入输出参数,建立了基于支持向量回归机的并联机床表面粗糙度预测模型。仿真实验的预测结果表明,所建立的预测模型具有较强的泛化能力,预测的准确性较高。
The parallel machine tool has many advantages compared with traditional machine tool,but the current research on parallel machine tool surface roughness related problems is less.In order to solve the problem of roughness,the paper puts forward a new prediction method of surface roughness of parallel machine tool based on support vector regression.After introducing the basic principles of the support vector regression machine,the paper establishes a surface roughness prediction model by using support vector machine regression.The training data are obtained from a certain type of parallel machine tool.The simulation results show that the prediction model has strong generalization ability,and the prediction accuracy is higher.
出处
《实验室研究与探索》
CAS
北大核心
2017年第1期30-33,37,共5页
Research and Exploration In Laboratory
基金
广东省青年创新人材项目(2014KONCX245)
关键词
并联机床
表面粗糙度
支持向量机
支持向量回归机
parallel machine tool
surface roughness
support vector machine
support vector regression
