A comparative study of two indirect methods to monitor surface integrity of ground components
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Data
2020-02-03
Autores
Hubner, Henrique Butzlaff
Silva, Rosemar Batista da
Viana Duarte, Marcus Antonio
Silva, Marcio Bacci da
Ferreira, Fabio Isaac [UNESP]
Aguiar, Paulo Roberto de [UNESP]
Baptista, Fabricio Guimaries [UNESP]
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Editor
Sage Publications Ltd
Resumo
One of the major challenges in grinding is to conciliate the material removal rate with components free from damages. Due to the poor thermal conductivity of conventional grinding wheels, most of the heat generated during grinding is transferred to the workpiece surface and subsurface, which can cause thermal damages and impair the performance in service of machined components. Hence, it is very important to monitor the grinding process to ensure the quality of the machined parts. Thus, this work presents an innovative study comparing two indirect monitoring methods in monitoring surface integrity of steel during grinding: the acoustic emission technique and the electromechanical impedance technique. Worktable speed and radial depth of cut were used as input parameters. Visual inspection and scanning electron microscopy images of ground surfaces as well as microhardness were the output variables used to assess surface integrity and to establish a relationship with the acoustic emission and electromechanical impedance techniques. Since the acoustic emission signals are non-stationary in nature, these signals were analyzed in the time-frequency domain by applying the short-time Fourier transform and the continuous wavelet transform. The root mean square deviation index was extracted as feature from the acoustic emission and electromechanical impedance signals. Results showed that both techniques presented similar results. The root mean square deviation index showed a good correlation with alterations in surface integrity under the conditions investigated.
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Process monitoring, acoustic emission, continuous wavelet transform, electromechanical impedance, surface integrity, root mean square deviation, short-time Fourier transform
Como citar
Structural Health Monitoring-an International Journal. London: Sage Publications Ltd, 15 p., 2020.