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Prediction of surface roughness using cutting parameters and vibration signals in minimum quantity coolant assisted turning of Ti-6Al-4V alloy

Author

Listed:
  • Vikas Upadhyay
  • P.K. Jain
  • N.K. Mehta

Abstract

In this work, an attempt has been made to investigate the role of vibration signals in prediction of surface roughness in minimum quantity coolant assisted turning of Ti-6Al-4V alloy. Initially, a model of surface roughness as a function of cutting parameters was developed to serve as the reference data. Subsequently, two more models were developed - one representing the variation of surface roughness with the vibration and the other represents the variation of surface roughness as a function of cutting parameters and vibration signal considered in tandem. A comparison of the three models established that the model based on simultaneous consideration of cutting parameters and vibration was the most accurate of the three.

Suggested Citation

  • Vikas Upadhyay & P.K. Jain & N.K. Mehta, 2013. "Prediction of surface roughness using cutting parameters and vibration signals in minimum quantity coolant assisted turning of Ti-6Al-4V alloy," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 27(1/2/3), pages 33-46.
    • Handle: RePEc:ids:ijmtma:v:27:y:2013:i:1/2/3:p:33-46
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    Cited by:

    1. Abdoulaye Diamoutene & Farid Noureddine & Rachid Noureddine & Bernard Kamsu-Foguem & Diakarya Barro, 2020. "Proportional hazard model for cutting tool recovery in machining," Journal of Risk and Reliability, , vol. 234(2), pages 322-332, April.
    2. Andhi Indira Kusuma & Yi-Mei Huang, 2023. "Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1683-1699, April.
    3. Richárd Horváth & Livija Cveticanin & Ivona Ninkov, 2022. "Prediction of Surface Roughness in Turning Applying the Model of Nonlinear Oscillator with Complex Deflection," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    4. PoTsang B. Huang, 2016. "An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 689-700, June.

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