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

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3 Author(s)
Dehui Wu ; Department of Electronic Engineering, Jiujiang University, Jiujiang 332005; School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009. E-mail: wdh ; Shiyuan Yang ; Hua Dong

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

Published in:

2006 6th World Congress on Intelligent Control and Automation  (Volume:1 )

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