Abstract
This present study focused on the effect of machining parameters such as helix angle of cutter, spindle speed, feed rate, axial and radial depth of cut on temperature rise in end milling. A prediction model of the temperature rise was developed using response surface methodology. The experiments were conducted on Al 6063 by high-speed steel end mill cutter based on central composite rotatable designs consisting of 32 experiments. The temperature rise was measured using K-type thermocouple. The adequacy of the model was verified using analysis of variance. The given model is utilized to analyze direct and interaction effect of the machining parameters with temperature rise. The optimization of machining process parameters to obtain minimum temperature rise was done using genetic algorithms. A source code using C language was developed to do the optimization. The obtained optimal machining parameters gave a value of 0.173 °C for minimum temperature rise.
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Palanisamy P, Rajendran I, Shanmugasundaram S, Saravanan R (2008) Prediction of cutting forcer and temperature rise in end-milling operation. Proc Inst Mech Eng B J Eng Manuf 220(10):1577–1587. doi:10.1243/09544054JEM542, 2006
Lazoglu I, Altintas Y (2002) Prediction of tool and chip temperature in continuous and interrupted machining. Int J Mach Tool Manuf 42:1011–1022
Leshock CE, Shin YC (1997) Investigation of cutting temperature in turning by a tool-work thermocouple technique. ASME J Manuf Sci Eng 119:502–508
Smart EF, Trent EM (1975) Temperature distributions in tools used for cutting iron, titanium and nickel. Int J Prod Res 13:265–290
Shaw MC (1984) Metal cutting principles. Oxford University Press, London
Zakaria AA, ElGomayel JI (1975) On the reliability of the cutting temperature for monitoring tool wear. Int J Mach Tool Design Res 15:195–208
Mathew P (1989) Use of predicted cutting temperatures in determining tool performance. Int J Mach Tool Manuf 29:481–497
Maekawa K, Kitagawa T, Kubo A (1997) Temperature and wear of cutting tools in high speed machining of Inconel 718 and Ti–6Al–6V–2Sn. Wear 202(2):142–148
Choudhury SK, Bartarya G (2003) Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. Int J Mach Tool Manuf 43:747–753
Dessoly, Melkote SN, Espalier C (2004) Modeling and verification of cutting tool temperatures in rotary tool turning of hardened steel. Int J Mach Tool Manuf 44:1463–1470
Haci S, Faruk U, Suleyman Y (2006) Investigation of the effect of rake angle and approaching angle on main cutting force and tool tip temperature. Int J Mach Tool Manuf 46:132–141
Weinert K, Brinkel F, Kempmann C, Pantke K (2007) The dependency of material properties and process conditions on the cutting temperatures when drilling polymers. Prod Eng Res Dev 1:381–387. doi:10.1007/s11740-007-0015-y
Geerdes WM, Alvardo MAT (2008) An application of physics based and artificial neural network-based hybrid temperature prediction scheme in a hot strip mill. J Manuf Sci Eng 130:014501
Zhang S, Liu ZQ (2008) An analytical model for transient temperature distributions in coated carbide cutting tools. Int Commun Heat Mass Transfer 35:1311–1315
Kadirgama K, Noor MM, Rahman MM, Harun WSW, Haron CHC (2009) Finite element analysis and statistical method to determine temperature distribution on cutting tool in end-milling. Eur J Sci Res 25(2):250–256
Suhail AH, Ismail N, Wong SV, Abdul Jalil NA (2010) Optimization of cutting parameters based on surface roughness and assistance of work piece surface temperature in turning process. Am J Eng Appl Sci 3(1): 102–108, ISSN 1941-7020
Liu YJ, Zhang JM, Wang SQ (2005) Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm. J Zhejiang Univ (Sci) 6A(10):1026–1029
Liu YM, Wang CJ (1999) A modified genetic algorithm based optimisation of milling parameters. Int J Adv Manuf Technol 15:796–799
Reddy NSK, Venkateswara Rao P (2006) Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms. Int J Adv Manuf Technol 28:463–473. doi:10.1007/s00170-004-2381-3
Palanisamy P, Rajendran I, Shanmugasundaram S (2007) Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int J Adv Manuf Technol. doi:10.1007/s00170-009-2104-x
Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput & Applic 18:135–140
Bharathi Raja S, Baskar N (2010) Optimization techniques for machining operations: a retrospective research based on various mathematical models. Int J Adv Manuf Technol 48:1075–1090. doi:10.1007/s00170-009-2351-x
Cochran WG, Cox GM (1963) Experimental design. Asia Publishing House, India
Montgomery DC (1976) Design and analysis of experiments. John Wiley and Sons, New York
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Pub, New York
Deb K (1995) Optimization for engineering design: algorithms and examples. Prentice-Hall, New Delhi
Donaldson C, Lecain GH, Goold VC (1957) Tool design. Tata McGraw-Hill, New Delhi
Stephenson DA, Agapiou JS (2006) Metal cutting theory and practice. Taylor & Francis, New York
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Sivasakthivel, P.S., Sudhakaran, R. Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 67, 2313–2323 (2013). https://doi.org/10.1007/s00170-012-4652-8
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DOI: https://doi.org/10.1007/s00170-012-4652-8