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Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

Author

Listed:
  • Joanna Kossakowska

    (Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland)

  • Sebastian Bombiński

    (Faculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, 26-600 Radom, Poland)

  • Krzysztof Ejsmont

    (Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland)

Abstract

There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine.

Suggested Citation

  • Joanna Kossakowska & Sebastian Bombiński & Krzysztof Ejsmont, 2021. "Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning," Energies, MDPI, vol. 14(20), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6489-:d:653063
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    References listed on IDEAS

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    1. Guofeng Wang & Yanchao Zhang & Chang Liu & Qinglu Xie & Yonggang Xu, 2019. "A new tool wear monitoring method based on multi-scale PCA," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 113-122, January.
    2. Zhao, G.Y. & Liu, Z.Y. & He, Y. & Cao, H.J. & Guo, Y.B., 2017. "Energy consumption in machining: Classification, prediction, and reduction strategy," Energy, Elsevier, vol. 133(C), pages 142-157.
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