Research of Tool Wear Predictive Technique Based on Support Vector Machine

Author

Liu Weiling ; Liu Libing ; Zhang Hongmei ; Yang Zeqing

Author_Institution

Sch. of Mech. Eng., Hebei Univ. of Technol., Tianjin, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

3

Abstract

The cutting tool condition monitoring technology is very important to the automated production, which is a small sample but very complicated system on account of the limited experiment data. In this paper, with feature extracted from the workpiece surface image, the tool wear predictive model is built based on SVM. The proposed method use Genetic Algorithm adjusts SVM kernel parameter. Experiment results show that the proposed model has a good recognition performance. Compared with BP ANN method, GA-SVM provides a better generalization capability and a lower prediction error.

Keywords

condition monitoring; cutting tools; feature extraction; genetic algorithms; production engineering computing; support vector machines; wear; BP ANN method; GA-SVM; automated production; cutting tool condition monitoring technology; feature extraction; genetic algorithm; support vector machine; tool wear prediction technique; workpiece surface image; Artificial neural networks; Condition monitoring; Cost function; Cutting tools; Genetic algorithms; Kernel; Mechanical engineering; Pattern recognition; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5304234

Filename

5304234