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Tool condition monitoring by SVM classification of machined surface images in turning

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Abstract

Tool condition monitoring has found its importance to meet the requirement of quality production in industries. Machined surface is directly affected by the extent of tool wear. Hence, by analyzing the machined surface, the information about the cutting tool condition can be obtained. This paper presents a novel technique for multi-classification of tool wear states using a kernel-based support vector machine (SVM) technique applied on the features extracted from the gray-level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull, and dull tool states by using Gaussian and polynomial kernels. The proposed method is found to be cost-effective and reliable for online tool wear classification.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, BIT, Mesra, Ranchi, 835215, India

    Nagaraj N. Bhat

  2. Precision Engineering and Metrology Group, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India

    Samik Dutta & Ranjan Sen

  3. Department of Electronics and Communication Engineering, BIT, Mesra, Ranchi, 835215, India

    Tarun Vashisth

  4. Department of Electrical Engineering, Shiv Nadar University, Dadri, Gautam Budh Nagar, Uttar Pradesh, 201314, India

    Srikanta Pal

  5. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Surjya K. Pal

Authors
  1. Nagaraj N. Bhat
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  2. Samik Dutta
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  3. Tarun Vashisth
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  4. Srikanta Pal
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  5. Surjya K. Pal
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  6. Ranjan Sen
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Corresponding author

Correspondence to Surjya K. Pal.

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Bhat, N.N., Dutta, S., Vashisth, T. et al. Tool condition monitoring by SVM classification of machined surface images in turning. Int J Adv Manuf Technol 83, 1487–1502 (2016). https://doi.org/10.1007/s00170-015-7441-3

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  • DOI: https://doi.org/10.1007/s00170-015-7441-3

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