Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Response Surface Methodology for Modeling and Optimization of Surface Roughness and Electric Current Consumption in Turning Operation

선삭 작업에서 표면조도와 전류소모의 모델링 및 최적화를 위한 반응표면방법론의 응용

  • Punuhsingon, Charles S.C. (Department of Systems Management & Engineering, Pukyong National University) ;
  • Oh, Soo-Cheol (Department of Systems Management & Engineering, Pukyong National University)
  • Received : 2014.06.02
  • Accepted : 2014.08.19
  • Published : 2014.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents an experiment on the modeling, analysis, prediction and optimization of machining parameters used during the turning process of the low-carbon steel known as ST40. The parameters used to develop the model are the cutting speed, the feed rate, and the depth of the cut. The experiments were carried out under various conditions, with three level of parameters and two different treatments for each level (with and without a lubricant), to determine the effects of the parameters on the surface roughness and electric current consumption. These effects were investigated using response surface methodology (RSM). A second-order model is used to predict the values of the surface roughness and the electric current consumption from the results of experiments which collected preliminary data. The results of the experiment and the predictions of the surface roughness and electric current consumption under both treatments were found to be nearly identical. This result shows that the feed rate is the main factor that influences the surface roughness and electric current consumption.

Keywords

References

  1. Ahilan at al., "Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools", Applied Soft Computing, Vol. 13, pp. 1543-1551, 2013. https://doi.org/10.1016/j.asoc.2012.03.071
  2. Benardos, P.G.,Vosniakos G.C, "Predicting surface roughness in machining: a review", International Journal of Machine Tools & Manufacture, Vol. 43, pp. 833-844, 2003. https://doi.org/10.1016/S0890-6955(03)00059-2
  3. Bhattacharya at al., "Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA", Prod. Eng. Res. Devel, Vol. 3, pp. 31-40, 2009. https://doi.org/10.1007/s11740-008-0132-2
  4. Davim at al. "Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models", Journal of materials processing technology, Vol. 205, pp. 16-23, 2008. https://doi.org/10.1016/j.jmatprotec.2007.11.082
  5. Ezilarasan at al., "An experimental analysis and measurement of process performances in machining of nimonic C-263 super alloy", Measurement, Vol. 46, pp. 185-199, 2013. https://doi.org/10.1016/j.measurement.2012.06.006
  6. Hamdan at al., "An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish", Int. J. Adv Manuf. Technol, Vol. 58, pp. 81-91, 2012. https://doi.org/10.1007/s00170-011-3392-5
  7. Jafarian at al., "Improving surface integrity in finish machining of Inconel 718 alloy using intelligent systems", Int. J. Adv Manuf. Technol, Vol. 71, pp. 817-827, 2014. https://doi.org/10.1007/s00170-013-5528-2
  8. Joardar at al., "Application of response surface methodology for determining cutting force model in turning of LM6/SiCP metal matrix composite", Measurement, Vol. 47, pp. 452-464, 2014. https://doi.org/10.1016/j.measurement.2013.09.023
  9. Kaynak., "Evaluation of machining performance in cryogenic machining of Inconel 718 and comparison with dry and MQL machining", Int. J. Adv Manuf. Technol, Vol. 72, pp. 919-933, 2014. https://doi.org/10.1007/s00170-014-5683-0
  10. Makadia and Nanavati, "Optimization of machining parameters for turning operations based on response surface methodology", Measurement, Vol. 46, pp. 1521-1529, 2013. https://doi.org/10.1016/j.measurement.2012.11.026
  11. Montgomery, C., "Design and Analysis of experiments", 4th ed. Wiley, New York, 1997.
  12. Neseli at al., "Optimization of tool geometry parameters for turning operations based on the response surface methodology", Measurement, Vol. 44, pp. 580-587, 2011. https://doi.org/10.1016/j.measurement.2010.11.018
  13. Sarikaya and Gullu, "Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL", Journal of Cleaner Production, Vol. 65, pp. 604-616, 2014. https://doi.org/10.1016/j.jclepro.2013.08.040
  14. Suresh at al., "Predictive modeling of cutting forces and tool wear in hard turning using response surface methodology", Procedia Engineering, Vol. 38, pp. 73-81, 2012. https://doi.org/10.1016/j.proeng.2012.06.011
  15. Singh and Rao, "A surface roughness prediction model for hard turning process", Int. J. Adv Manuf. Technol, Vol. 32, pp. 1115-1124, 2007. https://doi.org/10.1007/s00170-006-0429-2
  16. Palanikumar, "Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling", Int. J. Adv Manuf. Technol, Vol. 36, pp. 19-27, 2008. https://doi.org/10.1007/s00170-006-0811-0

Cited by

  1. 반응표면법을 사용한 고 중량물 낙하시험기의 충격에너지 흡수량 예측 연구 vol.15, pp.3, 2016, https://doi.org/10.14775/ksmpe.2016.15.3.044