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A review of flank wear prediction methods for tool condition monitoring in a turning process

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Abstract

Flank wear is the most commonly observed and unavoidable phenomenon in metal cutting which is also a major source of economic loss resulting due to material loss and machine down time. A wide variety of monitoring techniques have been developed for the online detection of flank wear. In order to provide a broad view of flank wear monitoring techniques and their implementation in tool condition monitoring system (TCMS), this paper reviews three key features of a TCMS, namely (1) signal acquisition, (2) signal processing and feature extraction, and (3) artificial intelligence techniques for decision making.

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Authors and Affiliations

  1. School of Mechanical & Chemical Engineering, The University of Western Australia, 35, Stirling Highway, Crawley, Western Australia, 6009, Australia

    A. Siddhpura & R. Paurobally

  2. CRC for Infrastructure and Engineering Asset Management (CIEAM), Brisbane, Queensland, Australia

    A. Siddhpura & R. Paurobally

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  1. A. Siddhpura
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  2. R. Paurobally
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Correspondence to A. Siddhpura.

Appendix

Appendix

Table 1 Publications which have used direct measurement methods
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Table 2 Publications which have used indirect measurement methods
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Table 3 Feature domain selection used by different publications
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Table 4 Different classifiers used by various publications
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Table 5 Year-wise summary of publications
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Siddhpura, A., Paurobally, R. A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65, 371–393 (2013). https://doi.org/10.1007/s00170-012-4177-1

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  • DOI: https://doi.org/10.1007/s00170-012-4177-1

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