Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks-Reference-Cited by-同舟云学术

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

Published:2020-08-28 Issue:7-8 Volume:110 Page:1833-1849
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

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

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-020-05902-w.pdf

Reference65 articles.

1. Alagumurthi N, Palaniradja K, Soundararajan V (2007) Heat generation and heat transfer in cylindrical grinding process - A numerical study. Int J Adv Manuf Technol 34:474–482. https://doi.org/10.1007/s00170-006-0619-y

2. Teixeira PHO, Rego RR, Pinto FW, de Oliveira Gomes J, Löpenhaus C (2019) Application of Hall effect for assessing grinding thermal damage. J Mater Process Technol 270:356–364. https://doi.org/10.1016/j.jmatprotec.2019.02.019

3. Deiva Nathan R, Vijayaraghavan L, Krishnamurthy R (1999) In-process monitoring of grinding burn in the cylindrical grinding of steel. J Mater Process Technol 91:37–42. https://doi.org/10.1016/S0924-0136(98)00408-7

4. Kwak JS, Ha MK (2004) Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. J Mater Process Technol 147:65–71. https://doi.org/10.1016/j.jmatprotec.2003.11.016

5. Santa-Aho S, Vippola M, Sorsa A et al (2012) Development of Barkhausen noise calibration blocks for reliable grinding burn detection. J Mater Process Technol 212:408–416. https://doi.org/10.1016/j.jmatprotec.2011.10.003

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