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Monitoring the cutting condition of structurally distinct aluminum oxide grinding wheels using acoustic emission signals and the Hinkley criterion

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Abstract

Acoustic emission sensors (AE) have been extensively utilized as an indirect method for condition monitoring of grinding wheels, the essential tools in the grinding process. Statistical parameters like root mean square (RMS) and counts have been employed to process these signals, aiming to characterize the cutting state of the wheel and determine the optimal moment for interrupting the dressing operation. However, the Hinkley criterion statistic, despite being employed in scientific studies such as structural health monitoring, has not yet been explored for monitoring the dressing operation of aluminum oxide wheels. In light of this, the present study aims to assess the efficacy of the Hinkley criterion statistic in extracting features from AE signals collected during the dressing operation of structurally distinct aluminum oxide wheels. Dressing tests were conducted using two wheels, each subjected to different dressing conditions. The AE sensor-generated signals were subsequently collected and digitally processed, followed by the computation of the Hinkley criterion statistic. In the time domain, the Hinkley criterion statistic enables the precise extraction of detailed information regarding the behavior of AE signals during grinding wheel dressing procedures, eliminating the need for intricate frequency domain analysis. The outcomes unequivocally demonstrate the effectiveness of the Hinkley criterion statistic in classifying the wheel as either dressed or undressed, thereby facilitating the determination of the optimal moment to halt the dressing operation. Importantly, the method proves its efficiency in categorizing the dressing condition of structurally diverse wheels, even when subjected to varying dressing parameters. Consequently, its implementation promises enhanced efficiency and cost-effectiveness in dressing operations. Furthermore, it is noteworthy that this method exhibits potential for generalization, making it suitable for monitoring the dressing process across a wide array of wheel types. Ultimately, this methodology plays a pivotal role in optimizing the grinding process.

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Acknowledgements

The authors would like to thank the Department of Electrical and Mechanical Engineering of the School of Engineering, Sao Paulo State University (UNESP), Bauru, São Paulo, and the Department of Control and Industrial Processes at the Federal Institute of Paraná - Jacarezinho campus.

Funding

The authors thank the 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 through Grant # 306435/2017–9 and Grant #306774/2021–6.

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Authors and Affiliations

  1. Paraná Federal Institute - IFPR campus Jacarezinho, Av Doutor Tito, 801, JD Panorama, Jacarezinho, PR, CEP: 86, 400-000, Brazil

    Wenderson Nascimento Lopes

  2. Department of Electrical Engineering, UNESP, Av. Eng. Luiz E. C. Coube, 14-01, CEP, Bauru, SP, 17033-360, Brazil

    Paulo Roberto de Aguiar & Zaqueu R. Fernando Antônio

  3. Pará Federal Institute - IFPA campus Parauapebas, PA 275 Km 68.8, União, Parauapebas, PA, CEP: 68, 515-000, Brazil

    Anderson Silva & Mauro Gomes da Silva

  4. Department of Informatics, Management and Design, CEFET-MG, R. Álvares de Azevedo, 400 - Bela Vista, Divinópolis, MG, CEP, 35503-822, Brazil

    Thabatta Moreira Alves de Araújo

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  1. Wenderson Nascimento Lopes
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  2. Paulo Roberto de Aguiar
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  3. Zaqueu R. Fernando Antônio
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  4. Anderson Silva
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  5. Mauro Gomes da Silva
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  6. Thabatta Moreira Alves de Araújo
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Wenderson Nascimento Lopes, Zaqueu R. Fernando Antônio, and Paulo Roberto Aguiar. The first draft of the manuscript was written by Wenderson Nascimento Lopes, Zaqueu R. Fernando Antônio, Anderson Silva, Mauro Gomes da Silva, and Thabatta Moreira Alves de Araújo, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wenderson Nascimento Lopes.

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Lopes, W.N., de Aguiar, P.R., Fernando Antônio, Z.R. et al. Monitoring the cutting condition of structurally distinct aluminum oxide grinding wheels using acoustic emission signals and the Hinkley criterion. Int J Adv Manuf Technol 131, 1071–1079 (2024). https://doi.org/10.1007/s00170-024-13139-0

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