Abstract
We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L 27 orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer-aided single-objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates (R 2 Ra =94.99 and R 2 Vb =92.73) that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process.
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References
A. G. Krishna, Optimization of surface grinding operations using a differential evolution approach, J. Mater. Process. Technol, 183 (2007) 202–209.
P. Krajnik, J. Kopac and A. Sluga, Design of grinding factors based on response surface methodology, J. Mater. Process. Technol, 162(163) (2005) 629–636.
J. S. Kwak, Application of Taguchi and response surface methodologies for geometric error in surface grinding process, Int. J. Mach. Tools & Manuf, 45(3) (2005) 327–334.
D. C. Montgomery, Design and Analysis of Experiments, (4th ed.) Wiley, New York, 1997.
H. W. Lee and W.T. Kwon, Determination of the minute range for RSM to select the optimum cutting conditions during turning on CNC lathe, 24(8) (2010) 1637–1645.
S. H. Baek, S. H. Hong, Seok-Swoo Cho, Deuk-Yul Jang and Won-Sik Joo, Optimization of process parameters for recycling of mill scale using Taguchi experimental design, 24(10) (2010) 2127–2134.
J. H. Jung and W. T. Kwon, Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis, 24(5) (2010) 1083–1090.
A. Aggarwal, H. Singh, P. Kumar and M. Singh, Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique-A comparative analysis, Journal of Materials Processing Technology, 200(1–3) (2008) 373–384.
X. Lin and H. Li, Enhanced Pareto particle swarm approach for multi-objective optimization of surface grinding process, Proceedings of the second International Symp. Intelligent Information Technology Application, Shanghai, China, (2008) 618–623.
J. Kopac, M. Bahor and M. Sokovic, Optimal machining parameters for achieving the desired surface roughness in fine turning of cold pre-formed steel workpieces, Int. J. Mach. Tools & Manuf, 42(6) (2002) 707–716.
M. Tomas and Y. Beauchamp, Statistical investigation of modal parameters of cutting tools in dry turning, Int. J. Mach. Tools & Manuf, 43(11) (2003) 1093–1106.
T. R. Lin, The use of reliability in the Taguchi method for the optimization of the polishing ceramic gauge block, Int. J. Adv. Manuf. Technol, 22(3–4) (2003) 237–242.
T. R. Lin, Experimental design and performance analysis of TiNcoated carbide tool in face milling stainless steel, J. Mater. Process. Technol, 127(1) (2002) 1–7.
K. M. Lee, M. R. Hsu, J. H. Chou and C. Y. Guo, Improved differential evolution approach for optimization of surface grinding process, Expert Syst. Appl., 38 (2011) 5680–5686.
A. G. Krishna and K. M. Rao, Multi-objective optimisation of surface grinding operations using scatter search approach, Int. J. Adv. Manuf. Technol. 29 (2006) 475–480.
S. S. Habib, Study of the parameters in electrical discharge machining through response surface methodology approach, Appl. Math. Model., 33(12) (2009) 4397–4407.
S. Agarwal and P. Venkateswara Rao, Modeling and prediction of surface roughness in ceramic grinding, Int. J. Mach. Tools & Manuf. 50 (2010) 1065–1076.
T. W. Hwang and S. Malkin, Upper bound analysis for specific energy in grinding of ceramics, Wear, 231(2) (1999) 161–171.
S. Shaji and V. Radhakrishnan, Analysis of process parameters in surface grinding with graphite as lubricant based of the Taguchi method, J. Mater. Process. Technol. 141(1) (2003) 51–59.
A. Jeang, Robust cutting parameters optimization for production time via computer experiment, Appl. Math. Model., 35(3) (2011) 1354–1362.
M. S. Lou, J. C. Chen and C. M. Li, Surface Roughness Prediction Technique For CNC End-Milling, Journal of Industrial Technology, 15(1) (1999).
S. Neşeli, S. Yaldız and E. Turkes, Optimization of tool geometry parameters for turning operations based on the response surface methodology, Measurement, 44 (2011) 580–587.
I. Asilturk and M. Cunkas, Modelling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method, Expert Syst. Appl., 38(5) (2011) 5826–5832.
http://www.predev.com/smg/parameters.htm, PDI Webmaster, Surface Metrology (2011, March 10).
O. Colak, C. Kurbanoğlu and M. C. Kayacan, Milling surface roughness prediction using evolutionary programming methods, Mater. Design, 28(2) (2007) 657–666.
I. Inasaki, Sensor fusion for monitoring and controlling grinding processes, Int. J. Adv. Manuf. Technol, 15 (1999) 730–736.
K. Dehghania, A. Nekahia and M. A. M Mirzaieb, Optimizing the bake hardening behavior of Al7075 using response surface methodology, Mater. Design, 31(4) (2010) 1768–1775.
M. C. Kathleen, Y. K. Natalia and R. Jeff, Response surface methodology, center for computational analysis of social and organizational systems (CASOS) Technical Report, 2004.
H. Raymond, C. Douglas, Montgomery and M. Christine Anderson-cook, process and product optimization using designed experiments, John Wiley & Sons, Inc. Third Edition, 2009.
K. M. Desai, S. A. Survase, P. S. Saudagar, S. S. Lele and R.S. Singhal, Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan, Biochem. Eng. J., 41 (2008) 266–270.
J. L. Rosa, A. Robin, M. B. Silva, C. A. Baldan, A. Carlos and M. P. Peres, Electrodeposition of copper on titanium wires: Taguchi experimental design approach, J. Mat. Processing Tech., 209 (2009) 1181–1188.
L. Celik, Monitoring vibration in grinding process and regression modelling of surface roughness, M. S. Thesis, Selcuk University, Turkey, 2010.
G. Taguchi, Introduction to quality engineering, Tokyo: Asian Productivity Organization, 1990.
I. Maros, Computational techniques of the simplex method, Kluwer Academic Publishers, 2003.
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Süleyman Neseli received his B.Sc. degree in Technical Education Faculty in Gazi University, Turkey, in 2002. He is currently a Ph.D student in the School of Mechanical Engineering, Selcuk University, Turkey. His research interests include chatter vibration control, process damping analysis and statistical optimization.
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Neşeli, S., Asiltürk, İ. & Çelik, L. Determining the optimum process parameter for grinding operations using robust process. J Mech Sci Technol 26, 3587–3595 (2012). https://doi.org/10.1007/s12206-012-0851-3
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DOI: https://doi.org/10.1007/s12206-012-0851-3