Multi-objective optimization of electrochemical machining parameters using response surface methodology

Authors

1 Mechanical Engineering group, Faculty of Engineering, University of Shahreza

2 Faculty of University of Guilan

Abstract

Electrochemical machining (ECM) process involves several physical and chemical phenomena that make it difficult to model the process.Therefore, selection of proper and optimal parameters setting is a challenging issue. In this paper, an approach is applied to look for the optimum solution to this problem. In this way, four parameters, i.e. voltage, tool feed rate, electrolyte flow rate and electrolyte concentration; and two machining criteria, i.e. material removal rate (MRR) and surface roughness (Ra) are considered as input variables and responses, respectively. Therefore, mathematical models have first been developed using response surface methodology (RSM). Then, the Derringer method has been utilized for optimizing the two responses simultaneously. MRR and Ra response would not be optimized in the same manner and have contradictive behaviors. The result of multi-objective optimization provides an optimal ECM process parameter setting, so the user can select desired optimal process parameters combination to achieve the optimal result. The optimal input parameters were determined as 25.56 V, 0.5 mm/min, 6.45 l/min, 138.1 g/l. Finally, optimization result was verified experimentally and the percentage error were 6.4 and 6.7 for MRR and Ra responses respectively.

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Main Subjects

References

[1]  Rumyantsev E, Davydov A (1989) Electrochemical machining of metals. MIR Publishers, Moscow.
[2]  Hinduja S, Kunieda M (2013)Modelling of ECM and EDM processes.CIRP Ann: Manuf Techn 62: 775–797.
[3] VenkataRao R, Kalyankar VD (2014) Optimization of modern machining processes using advanced optimization techniques: a review. Int J Adv Manuf Technol 73: 1159-1188.
[4]  Neto JCS, Silva EM, Silva MB (2006) Intervening variables in electrochemical machining. J Mater Process Technol179: 92–96.
[5]  Munda J,Malapati M, Bhattacharyya B (2007) Control of micro-spark and stray-current effect during EMM process.J Mater Process Technol194: 151–158.
[6]  Senthilkumar C, Ganesan G, KarthikeyanR (2011) Parametric optimization of electrochemical machining of Al/15% SiCp composites using NSGA-II.Trans Nonferrous Met Soc China 21: 2293-2300.
[7]  Chiou YC, Lee RT, Chen TJ, Chiou JM (2012) Fabrication of high aspect ratio micro-rod using a novel electrochemical micro-machining method. Precision Eng 36:193-202.
[8]  Bähre D, Weber O, Rebschläger A (2013) Investigation on pulse electrochemical machining characteristics of lamellar cast iron using a response surface methodology-based approach. Procedia CIRP 6: 362 – 367.
[9]  Klocke F, Zeis M, Klink A, Veselova D (2013) Experimental research on the electrochemical machining of modern titanium- and nickel-based alloys for aero engine components.Procedia CIRP 6: 368-374.
[10] Samanta S, Chakraborty S (2011)Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Eng Appl Artif Intell 24: 946-957.
[11]Landolt D, Chauvy PF, Zinger O (2003) Electrochemical micromachining, polishing and surface structuring of metals: fundamental aspects and new developments. Electrochimica Acta 48: 3185-3201.
[12] Bhattacharyya B, Munda J (2003) Experimental investigation on the influence of electrochemical machining parameters on machining rate and accuracy in micromachining domain. Int J Adv Manuf Technol 43: 1301-1310.
[13] Yusup N, Zain AM and Hashim SZM. Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011). Expert Syst Appl 2012; 39: 9909-9927.
[14] Moradi M, Ghoreishi M, Frostevarg J, Kaplan AFH (2013)An investigation on stability of laser hybrid arc welding. Optics and Lasers in Eng 5: 481–487.
[15]Sivaprakasam P, Hariharan P, Gowri S (2013) Optimization of Micro-WEDM Process of Aluminum Matrix Composite (A413-B4C): A Response Surface Approach. Mater Manuf Processes 28(12): 1340-1347.
[16] Montgomery DC (2009) Design and analysis of experiments. John Wiley, New York.
[17] MyersRH, Montgomery DC (1995) Response surface methodology: process and product optimization using designed experiments. Wiley, New York.
 [18] Derringer G, Suich R (1980) Simultaneous Optimization of Several Response Variables. J Quality Tech 12(4): 214-219.
[19] Castillo ED, Montgomery DC, Mc Carville DR (1996) Modified desirability functions for multiple response optimization. J Quality Tech 28(3): 337-345.
[20] AssarzadehS, Ghoreishi M (2013)A dual response surface-desirability approach to process modeling and optimization of Al2O3 powder-mixed electrical discharge machining (PMEDM) parameters.Int J Adv Manuf Technol 64: 1459-1477.
Journal of Solid and Fluid Mechanics
Volume 7, Issue 4
January 2018
Pages 49-60

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