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Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks

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Abstract

The grinding process is employed to provide a high-quality surface finish and tight dimensional tolerances to the manufactured components. However, it has the disadvantage of generating a large amount of heat during machining that is mostly transferred to the workpiece when employing conventional grinding wheels, which makes it highly susceptible to thermal damage. In terms of the different thermal damage associated with this process, grinding burn deserves special attention, as it affects the aesthetic aspect of the machined components. Since there is an increasing demand for productivity and high-quality products, the use of systems to monitor grinding burn becomes crucial when global competitiveness is in evidence. In this study, a novel approach, based on time-frequency images of acoustic emission signals and convolutional neural networks was proposed to monitor grinding burn. Experimental data were obtained from grinding tests on N2711 grade steel under different cutting conditions. Three different time-frequency analyses, including the short-time Fourier transform, the continuous wavelet transform, and the Hilbert-Huang transform, were used to generate the images that served as input for the CNN models. Through the proposed approach, grinding burn was successfully recognized, as the highest accuracy obtained by the models was 99.4% on the test dataset. This result is superior when considering those reported in the literature, in which conventional machine learning techniques are employed for grinding burn monitoring.

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Acknowledgments

The authors acknowledge the financial support of the Post Graduate Program of Mechanical Engineering of UFU.

Funding

This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001 that sponsored the scholarship of Henrique Butzlaff Hübner.

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  1. School of Mechanical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, 38408-100, Brazil

    Henrique Butzlaff Hübner, Marcus Antônio Viana Duarte & Rosemar Batista da Silva

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  1. Henrique Butzlaff Hübner
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Correspondence to Henrique Butzlaff Hübner.

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Hübner, H.B., Duarte, M.A.V. & da Silva, R.B. Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks. Int J Adv Manuf Technol 110, 1833–1849 (2020). https://doi.org/10.1007/s00170-020-05902-w

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