STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 6 , ISSUE 2 (April 2013) > List of articles

STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

Weilin Li * / Pan Fu * / Weiqing Cao / Weilin Li * / Pan Fu * / Weiqing Cao

Keywords : Tool condition monitoring, feature selection, multi-class support vector machine recursive feature elimination (SVM-RFE), least squares support vector machines (LS-SVM).

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 2, Pages 448-465, DOI: https://doi.org/10.21307/ijssis-2017-549

License : (CC BY-NC-ND 4.0)

Received Date : 24-January-2013 / Accepted: 12-March-2013 / Published Online: 10-April-2013

ARTICLE

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.

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REFERENCES

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