Intelligent prediction of surface roughness of milling aluminium alloy based on least square support vector machine

Author

Jiang, Zhuoda

Author_Institution

Key Lab. of Numerical Control of Jiangxi Province, Jiujiang Univ., Jiujiang, China

fYear

2010

fDate

26-28 May 2010

Firstpage

2872

Lastpage

2876

Abstract

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.

Keywords

Taguchi methods; aluminium alloys; design of experiments; least squares approximations; milling; support vector machines; surface roughness; Taguchi design of experiment; aluminium alloy milling; intelligent prediction; least square support vector machine; prediction error; surface roughness; Aluminum alloys; Feeds; Least squares methods; Machine intelligence; Milling; Predictive models; Rough surfaces; Support vector machines; Surface roughness; Testing; Aluminium alloy; Least square SVM (LS-SVM); Prediction model; Support vector machine(SVM); Surface roughness;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498687

Filename

5498687