Surface Roughness Prediction Model in Machining of 34CrMo4 Steel with Several Tools Using Response Surface Methodology

Abstract:

The surface roughness model in the turning of 34CrMo4 steel was developed in terms of cutting speed, feed rate and depth of cut and tool nose radius using response surface methodology. Machining tests were carried out using several tools with several tool radius under different cutting conditions. The roughness equations of cutting tools when machining the steels were achieved by using the experimental data. The results are presented in terms of mean values and confidence levels.The established equation and graphs show that the feed rate and cutting speed were found to be main influencing factor on the surface roughness. It increased with increasing the feed rate and depth of cut, but decreased with increasing the cutting speed, respectively. The variance analysis for the second-order model shows that the interaction terms and the square terms were statistically insignificant. However, it could be seen that the first-order affect of feed rate was significant while cutting speed and depth of cut was insignificant.The predicted surface roughness model of the samples was found to lie close to that of the experimentally observed ones with 95% confident intervals.

• Periodical: Advanced Materials Research (Volume 445)
• Pages: 90-95
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.445.90
• Online since: January 2012
• Authors: Hamed Barghikar, Amin Poursafar, Abbas Amrollahi
• Keywords: 34CrMo4, Lath Machining, Response Surface Method (RSM), Surface Roughness Model

References

[1] I. Mukherje, P.K. Ray: A Review of optimization techniques in metal cutting processes (Computers and Industrial Engineering 50, 2006).

[2] Tan F. P, Cresse, and R. C: generalized multi-pass machining model for machining parameters selection in turning (International Journal of Production Research, 1995).

DOI: 10.1080/00207549508930221

[3] Sahin Y., A.R. Motorcu: Surface roughness prediction model in machining of carbon steel by PVD coated cutting tools (Journal of applied sciences, 2004).

DOI: 10.3844/ajassp.2004.12.17

[4] Ozel T., Y. Karpat: Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks (Journal of machine tools manufacturing, 2005).

DOI: 10.1016/j.ijmachtools.2004.09.007

[5] D. C Montgomery, in: Design and analysis of Experiments, 4th edition, , wiley Publishers, New York (1997).

[6] M. Thomas, Y. Beauchamp, Y.A. Youssef, and J. Masounave: An experimental design for surface roughness and built-up edge formation in lathe dry turning (Journal of Quality Science, 1997).

DOI: 10.1108/13598539710170803

[7] W.H. Yang, Y.S. Tarng: Design optimization of cutting parameters for turning operations based on Taguchi method (Journal of Material Processing Technology, 1998).

DOI: 10.1016/s0924-0136(98)00079-x

[8] J. Wang: The effect of the multi-layer surface coating of carbide inserts on the cutting forces in turning operations (Journal of Material Processing Technology, 2000).

DOI: 10.1016/s0924-0136(99)00353-2

[9] M.Y. Noordin, V.C. Venkatesh, C.W. Lok, S. Sharif, A. Abdullah: Performance evaluation of cemented carbide tools in turning AISI 1010 steel (Journal of Material Processing Technology, 2001).

DOI: 10.1016/s0924-0136(01)00838-x

[10] D. Gillibrand, M. Sarwar, C.T. Pierce: The economic benefit of finish turning with coated carbide (Journal of Surface Coating Technology, 1996).

DOI: 10.1016/s0257-8972(96)03059-9