Monitoring the dressing operation of conventional aluminum oxide grinding wheels through damage index, power spectral density, and piezoelectric sensors

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorScience and Technology-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorRuas, Erick Luiz Vieira-
Autor(es): dc.creatorLopes, Wenderson Nascimento-
Autor(es): dc.creatorde Aguiar, Paulo Roberto-
Autor(es): dc.creatorLopes, Thiago Glissoi-
Autor(es): dc.creatorJunior, Pedro Oliveira Conceição-
Data de aceite: dc.date.accessioned2025-08-21T17:56:38Z-
Data de disponibilização: dc.date.available2025-08-21T17:56:38Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00170-023-11682-w-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/247543-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/247543-
Descrição: dc.descriptionMonitoring the dressing operation of grinding wheels is crucial for optimizing the grinding process and ensuring quality outcomes. This study presents a novel data-driven method utilizing piezoelectric diaphragm signals, combined with the root mean square deviation index (RMSD) and power spectral density (PSD), to determine the optimal moment for interrupting the dressing operation of conventional aluminum oxide grinding wheels. By addressing the existing gaps in transition methods between dressed and undressed grinding wheels, as well as exploring untested metrics in digital signal processing, this research expands the use of alternative piezoelectric transducers for monitoring dressing. The proposed methodology utilizes a commercial acoustic emission (AE) sensor as a reference and employs experimental dressing tests to validate its effectiveness. The signals from both the AE sensor and the piezoelectric diaphragm are collected and subjected to digital processing to extract relevant features based on the proposed approach. Results demonstrate that the RMSD index successfully extracts information about the cutting surface conditions of the grinding wheel from signals obtained by both AE sensors and piezoelectric diaphragms. Furthermore, by selecting frequency bands that exhibit strong correlations with the grinding wheel’s cutting surface conditions, a threshold is defined, enabling timely interruption of the dressing operation, thereby ensuring the restoration of the grinding wheel for continued use in grinding applications. Ultimately, this study showcases the feasibility of a non-invasive method for monitoring the dressing operation of conventional grinding wheels, contributing significantly to the optimization of the grinding process.-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University (UNESP), Av. Eng. Luiz E. C. Coube, 14-01, SP-
Descrição: dc.descriptionParaná Federal Institute of Education Science and Technology, Avenida Dr. Tito, 801, Jardim Panorama, PR-
Descrição: dc.descriptionDepartment of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo USP. Av. Trab. São Carlense, 400 - Pq. Arnold Schimidt, SP-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University (UNESP), Av. Eng. Luiz E. C. Coube, 14-01, SP-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Advanced Manufacturing Technology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDamage index-
Palavras-chave: dc.subjectDressing operation-
Palavras-chave: dc.subjectIndirect monitoring-
Palavras-chave: dc.subjectPiezoelectric sensors-
Título: dc.titleMonitoring the dressing operation of conventional aluminum oxide grinding wheels through damage index, power spectral density, and piezoelectric sensors-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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