Title :
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.
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;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1713290
