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An efficient short-time Fourier transform algorithm for grinding wheel condition monitoring through acoustic emission

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Abstract

Indirect methods to monitor the surface integrity of grinding wheels by acoustic emission (AE) have been proposed, aiming to ensure their optimal performance. However, the time-frequency analysis of the content of these signals has not been addressed in the literature. AE signal analysis performed only in the frequency domain makes it impossible to locate faults on the grinding wheel surface during the dressing operation and examine the behavior of the frequencies contained in these signals over time. In this regard, the time-frequency analysis of AE signals during dressing through STFT (short-time Fourier transform) can contribute toward the proposal of new monitoring methodologies, thus reflecting the optimization of the grinding process. This paper proposes an algorithm based on the Kaiser window to adjust the STFT parameters to ensure an appropriate balance between time-frequency resolutions. Besides, this algorithm is used to investigate the characteristic frequencies in the aluminum oxide grinding wheel in dressing operation. The results indicate that the spectral content of the AE signals during dressing follows a uniform behavior, but their amplitude changes depending on the characteristics of topography and sharpness of the grinding wheel cutting edges.

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Data Availability

The datasets that support the findings of this study are not publicly available due to their content access be restricted to the research group participants. All the material and procedures that support the findings of this study were reported in the Section 3—Material and methods.

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Acknowledgements

The authors would like to thank NORTON of Saint Gobain group for the donation of the grinding wheels.

Funding

The authors thank CAPES (Coordination for the Improvement of Higher Level Education Personnel), and CNPq (National Council for Scientific and Technological Development) for their financial support of this research.

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Authors and Affiliations

  1. Electrical Engineering Department, São Paulo State University (UNESP), Av. Eng. Luiz Edmundo C. Coube 14-01, Bauru, Brazil

    Wenderson N. Lopes, Pedro O. C. Junior, Paulo R. Aguiar, Felipe A. Alexandre, Fábio R. L. Dotto, Paulo Sérgio da Silva & Eduardo C. Bianchi

  2. Control and Automation Engineering Department, Pará Federal Institute of Education, Science and Technology (IFPA), Campus Parauapebas, PA 275, s/n, União, Parauapebas, Brazil

    Wenderson N. Lopes

  3. Mechanical Engineering Department, São Paulo State University (UNESP), Av. Eng. Luiz Edmundo C. Coube 14-01, Bauru, Brazil

    Eduardo C. Bianchi

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  1. Wenderson N. Lopes
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Contributions

P. R. Aguiar designed the experiments; W. N. Lopes performed the experiments and processed the digital signals; P. O. C. Junior and F. A. Alexandre supported the discussion of the results. P. S. Silva supported the fundamental developments of the proposed algorithm; and F. R. L. Dotto and E. C. Bianchi supported the material and methods and overviewed the whole work. All authors read and approved the final manuscript.

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Correspondence to Wenderson N. Lopes.

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Lopes, W.N., Junior, P.O.C., Aguiar, P.R. et al. An efficient short-time Fourier transform algorithm for grinding wheel condition monitoring through acoustic emission. Int J Adv Manuf Technol 113, 585–603 (2021). https://doi.org/10.1007/s00170-020-06476-3

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