Prediction Method for Machining Quality Based on Weighted Least Squares Support Vector Machine

Author

Wu, Dehui ; Yang, Shiyuan ; Dong, Hua

Author_Institution

Dept. of Electron. Eng., Jiujiang Univ.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4776

Lastpage

4780

Abstract

A new machining error prediction approach, which is based on the weighted least squares support vector machine (LS-SVM), was given. The nearer sample was set a larger weight, while the farther was set the smaller weight in the history data. In the same condition, the results show that the prediction accuracy of the weighted LS-SVM is 40% higher than that of the standard LS-SVM. Compared with other more modeling approaches, the prediction effect indicates that the proposed method is more accurate and can be realized more easily. It provides a better way for on-line quality monitoring and controlling of dynamic machining

Keywords

forecasting theory; machining; quality control; support vector machines; dynamic machining control; forecasting model; machining error prediction; machining quality; on-line quality monitoring; quality control; weighted least squares support vector machine; Accuracy; Artificial neural networks; Condition monitoring; History; Instruments; Least squares methods; Machining; Prediction methods; Predictive models; Support vector machines; forecasting model; machining quality; quality control; weighted least squares support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713290

Filename

1713290