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Modeling Of Metal Cutting Process Using Response Surface Methodology

J. Edwin Raja Dhas, R.S. Stalin


The goal of modern industry is to manufacture low cost, high quality products in short time. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. A number of researchers have dealt with the optimization of machining parameters, considering only turning operations and graphical methods to determine the optimum speed, feed and depth of cut. In this work, work pieces machined by Computer Numerical Control machine centre are evaluated according to Response Surface Methodology with an objective function of obtaining good surface finish using single tool operations. Optimum machining parameters resulting from this work are intended for use by Numerical Control machines in order to improve machining efficiencies, improve quality, and reduce rework and scrap. 

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Bralla J. G, (1998), Design for Manufacturability Handbook. 2nd edition, New York: McGraw-Hill.

Chern G, (1993), Analysis of burr formation and Breakout in metal cutting. Ph.D. dissertation, University of California—Berkeley.

Kalpakjian S. and Schmid S, (2000), Manufacturing Engineering and Technology. 4th edn, Upper Saddle River, NJ: Prentice Hall.

Oxley P.L.B. (1988), “Modelling machining processes with a view to their optimization and the adaptive control of metal cutting machine tools” Robot. Comput.-Integrated Manuf, Vol 4, pp103–119.

Chryssolouris G and Guillot M, 1990) A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining, ASME J. Eng. Ind. Vol 112, pp122–131.

Abuelnaga, A.M and El-Dardiry M.A, (1984) Optimization methods for metal cutting, Int. J. Mach. Tool Des. Res. Vol 24 No1, pp 1–18.

Zhou C and Wysk R.A, (1992), “An integrated system for selecting optimum cutting speeds and tool replacement times” International Journal of Machine Tools and Manufacture, Vol 32, pp 695–707.

Yao Y, and Fang X.D, (1992), “Modelling of multivariate time series for tool wear estimation in finish turning” Int. J. Mach. Tools Manuf. Vol 32, No 4, pp 495–508.

Hashimura, M. Hassamontr. J and Dornfeld. D, (1999), “Effect of in-plane exit angle and rake angles on burr height and thickness in face milling operation” ASME, Journal of Manufacturing Science & Engineering, Vol ,121, pp 13–18.

Hassamontr. J, (1998), Edge finishing planning in milling. Ph.D. dissertation, University of California—Berkeley

Olvera, O and Barrow. G, (1996), “An experimental study of burr formation in square shoulder face milling. International Journal of Machine Tools Manufacturing, Vol 36, No 9 ,pp 1005–1020.

Olvera, O and Barrow. G, (1998), “Influence of exit angle and tool nose geometry on burr formation in face milling operations” Proceedings of the Institute of Mechanical Engineers, Vol 212 (Part B), pp. 59–72

Manna, A and Bhattacharyya. B, (2004), “Investigation for optimal parametric combination for achieving better surface finish during turning of Al/SiC-MMC” Int. J. Adv. Manuf. Technol. Vol 23, pp 658–665.

Ross, P.J. (1988) Taguchi Techniques for Quality Engineering, Mc- Graw-Hill, New York.

Myers, R. H and Montgomery, D. C, (1995) Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley).

Box G. E. P and Draper N. R. (1987), "Empirical Model Building and Response Surfaces," John Wiley and Sons, New York.

Rajesh Prabha N and J. Edwin Raja Dhas (2017) Design optimization of surface roughness by turning process using response surface methodology and grey relational analysis International Journal of Mechanical Engineering and Technology Volume 8, Issue 8, pp. 810–810.

Ramanan G and Edwin Raja Dhas J (2017) Multi Objective Optimization of Machining Parameters for AA7075 Metal Matrix Composite Using Grey - Fuzzy Technique International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 8 pp. 1729-1735.

J. Edwin Raja Dhas and P.Pradeep ,(2015) Application of RSM and ANFIS models for machining Quality Prediction, Alloy Journal of Soft Computing and Applications Volume 3, Issue 1 pp.5-13.

J.Edwin Raja Dhas, R.S. Stalin and J. Rajeesh (2013) RBF neural network model for machining quality prediction International Journal of Modeling Identification and Control Vol 20, No 2, 174-180.


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