Intelligent Prediction of Surface Roughness of Milling Aluminium Alloy Based on Least Square Support Vector Machine

摘要：

An intelligent model is developed to predict the surface roughness of aluminium alloy in the milling operation based on least square support vector machine (LS-SVM). The Taguchi’s design of experiment was adopted to provide enough training information with minimal experiment times. The present prediction model is to analyze the effects of condition factors, such as spindle speed, feed rate, etc. on the surface roughness (Ra). The tests have been conducted to verify the LS-SVM model, and the average prediction error is about 8％. It means the model is capable to predict the surface roughness well.

关键词： Prediction model Surface roughness Aluminium alloy Support vector machine(SVM) Least square SVM (LS-SVM)

作者: Zhuoda Jiang

作者单位: Key Laboratory of Numerical Control of Jiangxi Province, Jiujiang University, Jiujiang, Jiangxi, 332005, China School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 2872-2876

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）