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Face milling tool condition monitoring using sound signal

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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.

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Acknowledgement

The author/s acknowledge the contribution of Centre for System Design (CSD): A Centre of Excellence at NITK Surathkal pertaining to issuance of technical equipment/product service/experimental facility. The technical support received from the members of SOLVE: The Virtual Lab at NITK Surathkal is deeply appreciated. The authors also acknowledge help rendered by Dr. V. Sugumaran, Associate Professor, VIT University, Chennai.

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Authors and Affiliations

  1. National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    C. K. Madhusudana, Hemantha Kumar & S. Narendranath

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  1. C. K. Madhusudana
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  2. Hemantha Kumar
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  3. S. Narendranath
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Correspondence to Hemantha Kumar.

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Madhusudana, C.K., Kumar, H. & Narendranath, S. Face milling tool condition monitoring using sound signal. Int J Syst Assur Eng Manag 8 (Suppl 2), 1643–1653 (2017). https://doi.org/10.1007/s13198-017-0637-1

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  • DOI: https://doi.org/10.1007/s13198-017-0637-1

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