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Inferential measurement of the dresser width for the grinding process automation

  • Fabio Isaac FerreiraEmail author
  • Paulo Roberto de Aguiar
  • Wenderson Nascimento Lopes
  • Cesar Henrique Rossinoli Martins
  • Rodrigo de Souza Ruzzi
  • Eduardo Carlos Bianchi
  • Doriana Marilena D’Addona
ORIGINAL ARTICLE
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Abstract

Dressing is an essential process for the machining industries. The grinding community keeps the slogan “grinding is dressing,” given the importance of this reconditioning process. This paper presents a methodology for forecasting the dresser width one step forward by using indirect monitoring. The dresser width is an important parameter to guarantee the quality of the dressing process and, in many cases, it is monitored directly by the operators. Acoustic emission signals were collected during the dressing process and an estimation neural network was used to correlate the dresser width with the processed signals to estimate the current value of the width. The output of the estimation network was input to a time-delay neural network to predict the next value of the dresser width. By utilizing this procedure, an automatic system would be able to readjust the dressing parameters while avoiding the stops, reducing costs, and maintaining repeatability during the process.

Keywords

Inferential measurement Acoustic emission Artificial neural networks Tool wear condition Dressing operation 

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Notes

Acknowledgment

The authors would like to thank NORTON, Saint Gobain group, for the donation of the grinding wheels, and the Master Diamond Ferramentas Ltda for the fabrication of the dressers. Also, thanks go to Capes and CNPq for supporting this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Fabio Isaac Ferreira
    • 1
    Email author
  • Paulo Roberto de Aguiar
    • 1
  • Wenderson Nascimento Lopes
    • 1
  • Cesar Henrique Rossinoli Martins
    • 2
  • Rodrigo de Souza Ruzzi
    • 3
  • Eduardo Carlos Bianchi
    • 4
  • Doriana Marilena D’Addona
    • 5
  1. 1.Department of Electrical Engineering, Faculty of Engineering Bauru (FEB)Universidade Estadual Paulista (UNESP)BauruBrazil
  2. 2.Department of Electrical and Computational EngineeringSão Paulo University (USP)São CarlosBrazil
  3. 3.School of Mechanical EngineeringFederal University of Uberlândia (UFU)UberlândiaBrazil
  4. 4.Department of Mechanical Engineering, Faculty of Engineering Bauru (FEB)Universidade Estadual Paulista (UNESP)São PauloBrazil
  5. 5.Department of Chemical, Materials and Production EngineeringNapoli Federico II University (UNINA)NaplesItaly

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