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Face milling tool condition monitoring using sound signal

Author

Listed:
  • C. K. Madhusudana

    (National Institute of Technology Karnataka)

  • Hemantha Kumar

    (National Institute of Technology Karnataka)

  • S. Narendranath

    (National Institute of Technology Karnataka)

Abstract

This article presents the fault diagnosis of the face milling tool using sound signal. During milling, sound signals of the face milling tool under healthy and fault conditions are acquired. Discrete wavelet transform (DWT) features are extracted from the acquired sound signals. The support vector machine (SVM) technique is used to classify the face milling tool conditions using the extracted DWT features. Also, a comparison of classification efficiencies of different classifiers with respect to different features extraction methods is carried out. It is shown that, all extracted DWT features demonstrate better results than those obtained from selected statistical features and empirical mode decomposition features. The SVM technique is the best classifier as it has given an encouraging result in this study when compared to other classifiers, and it has provided 83% classification accuracy for the given experimental conditions and workpiece of steel alloy 42CrMo4. Hence, the SVM method and DWT technique can be put forward for the applications of condition monitoring of the face milling tool with sound signal.

Suggested Citation

  • C. K. Madhusudana & Hemantha Kumar & S. Narendranath, 2017. "Face milling tool condition monitoring using sound signal," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1643-1653, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-017-0637-1
    DOI: 10.1007/s13198-017-0637-1
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    Cited by:

    1. K. N. Ravikumar & Suhas S. Aralikatti & Hemantha Kumar & G. N. Kumar & K. V. Gangadharan, 2022. "Fault diagnosis of antifriction bearing in internal combustion engine gearbox using data mining techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1121-1134, June.
    2. Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.

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