<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper presents an on-line tool wear condition monitoring system for milling. The proposed system was developed taking the cost and performance in practice into account, in addition to a high success rate. The cutting vibration signal is obtained during the cutting process, and then extracting features using time-domain statistical and wavelet packet decomposition algorithms. It would result in two major disadvantages if creating a tool wear states identification model based on all extracted features, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. It is crucial to extract a smaller feature set by an effective feature selection algorithm. In this paper, an approach based on one-versus-one multi-class Support Vector Machine Recursive Feature Elimination (SVM-RFE) is proposed to solve the feature selection problem in tool wear condition monitoring. Moreover, in order to analyze a performance degradation process on the machine tool, Least Squares Support Vector Machines (LS-SVM) is introduced. In order to estimate the effectiveness of feature selection algorithm, the comparative analysis among Fisher Score (FS) Information Gain (IG) and SVM-RFE is exploited to real milling datasets. The identification result proves that: The selected feature set based on SVM-RFE is more effective to recognize tool wear state; LS-SVM wear identification method is superior to BP neural network, and it has higher identification accuracy; the proposed feature selection and identification method for tool wear states is efficient and feasible.</p>
</abstract>

This paper presents an on-line tool wear condition monitoring system for milling. The proposed system was developed taking the cost and performance in practice into account, in addition to a high success rate. The cutting vibration signal is obtained during the cutting process, and then extracting features using time-domain statistical and wavelet packet decomposition algorithms. It would result in two major disadvantages if creating a tool wear states identification model based on all extracted features, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. It is crucial to extract a smaller feature set by an effective feature selection algorithm. In this paper, an approach based on one-versus-one multi-class Support Vector Machine Recursive Feature Elimination (SVM-RFE) is proposed to solve the feature selection problem in tool wear condition monitoring. Moreover, in order to analyze a performance degradation process on the machine tool, Least Squares Support Vector Machines (LS-SVM) is introduced. In order to estimate the effectiveness of feature selection algorithm, the comparative analysis among Fisher Score (FS) Information Gain (IG) and SVM-RFE is exploited to real milling datasets. The identification result proves that: The selected feature set based on SVM-RFE is more effective to recognize tool wear state; LS-SVM wear identification method is superior to BP neural network, and it has higher identification accuracy; the proposed feature selection and identification method for tool wear states is efficient and feasible.

Study on Feature Selection and Identification Method of Tool Wear States Based on Svm

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper presents an on-line tool wear condition monitoring system for milling. The proposed system was developed taking the cost and performance in practice...

Study on Feature Selection and Identification Method of Tool Wear States Based on Svm

Online veröffentlicht: 10. Apr. 2013

Seitenbereich: 448 - 465

Eingereicht: 24. Jan. 2013

Akzeptiert: 12. März 2013

DOI: https://doi.org/10.21307/ijssis-2017-549

© 2013 Weilin Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.