The effect of electrical discharge machining parameters on alloy DIN 1.2080 using the Taguchi method and determinant of optimal design of experiments
Keywords:Electrical discharge machining, D3 steel, Taguchi, D-optimal, regression modeling, optimization.
Electrical Discharge Machining (EDM) process is one of the most widely used methods for machining. This method is used to form parts that conduct electricity. This method of machining has used for hard materials and therefore select the correct values of parameters are so effective on the quality machining of parts. D3 steel has a high abrasion resistance at low temperatures therefore can be a good candidate for this method of machining. Also because of high hardness and low distortion during heat treatment, using this method is economical for this alloy. The purpose of this paper is to investigate the influence of the main parameters such as voltage, current, pulse duration and pulse off time and the interaction of them to determine the optimal condition for the D3 steel alloy (alloy with DIN 1.2080). Chip removal rate (MRR) and surface quality of parts were evaluated as the output characteristic of the study. The optimum conditions were achieved when the MRR is in the highest value and surface roughness is in the lowest one. For investigation of interaction, two kinds of DOE methods (Taguchi and determinant of optimal experimental design) are used. Then the optimal parameters are investigated with the help of the analysis signal to noise (S/N) and mathematical modeling. The optimize results were tested again and compared. Also the results showed that regression modeling has better accuracy than the S/N analysis. This is because of a greater number of experiments that done in this part and taking into account the interaction parameters in the regression model.
Kalpakjian S, (1995), Manufacturing Engineering and Technology, Addison-Wesley.
Semiromi, DT, Azimian, AR (2011), Molecular dynamics simulation of nonodroplets with the modified Lennard-Jones potential function, Heat and mass transfer, 47(5):579-588.
Zarringhalam, M, Karimipour, A, Toghraie, D, (2016), Experimental study of the effect of solid volume fraction and Reynolds number on heat transfer coefficient and pressure drop of CuOwater nanofluid, Experimental Thermal and Fluid Science, 76:342-351.
Nourouzi, S, Kolahdooz, A, Botkan, M, (2012), Behavior of A356 Alloy in semi-solid state produced by mechanical stirring, In Advanced Materials Research (Vol. 402, pp. 331-336). Trans Tech Publications.
Hashemzadeh, H, Eftekhari, SA, Loh-Mousavi, M, (2017), Forging pre-form dies optimization using artificial neural networks and continuous genetic algorithm, An International Peer Reviewed Open Access Journal For Rapid Publication, 74.
Esfe, MH, Afrand, M, Gharehkhani, S, Rostamian, H, Toghraie, D, Dahari, M, (2016), An experimental study on viscosity of alumina-engine oil: effects of temperature and nanoparticles concentration, International Communications in Heat and Mass Transfer, 76:202-208.
Jafari Dinani M, Kolahdooz A, Eftekhari SA (2016), Investigation of notching HSS rolls with high hardness for hot mill rolling, Journal of Mechanical Engineering and Vibration, 7(2):7-14.
Uhlmann E, Domingosb DC, (2013), Development and optimization of the die-sinking EDM technology for machining the nickel-based alloy MAR-M247 for turbine components, Pro CIRP, 6:180-185.
Ayestaa, Izquierdob B, (2013), Influence of EDM parameters on slot machining in C1023 aeronautical alloy, Pro CIRP, 6:129-134.
Gopakalannan S, Sinthelevan T, (2012), Modeling and Optimization of EDM Process parameter on Machining of AL 7075-B4 MMC using RSM, Pro Eng, 38:685-690.
Clijsters S, Liu K, (2010), EDM technology and strategy development for the manufacturing of complex parts in SiSiC, J Mat Proc Tech, 210:631-641.
Tzeng YF, (2008), Development of a flexible high-speed EDM technology with geometrical transform optimization, J Mat Proc Tech, 203:355-364.
Rajmohan T, Prubho R, (2012), Optimization of Machining parameter in EDM of 304 Stainless Steel, Pro Eng, 38:1030-1036.
Zarepour H, Fadaei Tehrani A, (2007), Statistical analysis on electrode wear in EDM of tool steel DIN 1.2714 used in forging dies, J Mat Proc Tech, 187-188:708-714.
Tzeng YF, Chen F, (2007), Multi-objective optimization of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach, Mat & Des, 28:1159-1168.
Manikandan R., Venkatesan R., (2012), Optimizing the Machining Parameters of Micro-EDM for Inconel 718, J App Sci, 12:971-977.
Dave HK, Desai KP, Raval HK, (2012), Modeling and Analysis of Material Removal Rate During Electro Discharge Machining of Inconel 718 under Orbital Tool Movement, Int J Manu Sys, 2:12-20.
Yazdi, M, Latifi Rostami, S, Kolahdooz, A, (2016), Optimization of geometrical parameters in a specific composite lattice structure using neural networks and ABC algorithm, Journal of Mechanical Science & Technology, 30(4):17631771 .
Mirkalantari, SA, Hashemian, M, Eftekhari, SA, Toghraie, D, (2017), Pull-in Instability Analysis of Rectangular Nanoplate based on Strain Gradient Theory Considering Surface Stress Effects, Physica B: Condensed Matter. 519:1-14.
Vishwakarma M, Parashar V, Khare VK, (2012), Regression Analysis and Optimization of Material Removal Rate on Electric Discharge Machining for EN-19 alloy steel, Int J Sci Res Pub, 2:1-7.
Kolahdooz, A, Nourouzi, S, Jooybari, MB, Hosseinipour, SJ, (2014), Experimental investigation of thixoforging parameters effects on the microstructure and mechanical properties of the helical gearbox cap, Journal of Mechanical Science and Technology, 28(10):4257.
Genichi Taguchi, (2005), Taguchi quality engineering handbook, New Jersey.
Rajmohan T, Prubho R, (2012), Optimization of Machining Parameter in EDM of 304 Stainless Steel, Proc Eng, 38:1030-1036.
MINITAB 16.2.4 Manual, Minitab Inc. (2015)