International Journal of Advance Engineering and Research Development. Optimization of MRR in Sinker EDM Process Using Genetic Algorithm Technique

Transcription

1 Scientific Journal of Impact Factor (SJIF): 4.14 International Journal of Advance Engineering and Research Development Volume 3, Issue 3, March e-issn (O): p-issn (P): Optimization of MRR in Sinker EDM Process Using Genetic Algorithm Technique 1 Virendra Kumar Gupta, 2 Mr. R.B. Prasad 1 M.Tech Scholar Mechanical Engineering Department, M.M.M.U.T. Gorakhpur, U.P., India 2 Assistant Professor Mechanical Engineering Department, M.M.M.U.T. Gorakhpur, U.P., India Abstract The electro discharge machining operations are commonly characterized by high energy consumptions and the most matter-of-fact is non-traditional machining methods which are not subjective by the hardness and physical properties of the work piece. In this research we study to the effects of sinker EDM parameters such as voltage, pulse on time and pulse off time on the process parameter like material removal rate. Better machining performance is obtained generally with the electrode as the cathode and the work piece as an anode. EDM process is an experience process, wherein still the selected parameters are often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. Material Removal Rate (MRR) should maximum during this process for high productivity estimate with the aim to maximize. Then objective function is created by regression analysis and optimized by Genetic Algorithm technique. The model is shown to be effective; MRR improved using optimized machining parameters. Index Terms EDM, MRR, Voltage, Pulse on and off time, Regression analysis, Genetic Algorithm optimization. INTRODUCTION Electrical Discharge Machining (EDM) is a nontraditional manufacturing process and it is a process of material removal using accurately controlled erosion by a series of electric sparks through a small gap (approximately10 to 50 microns) filled with dielectric fluid between an electrode and a work piece. The hardness of the work piece has no effect on the process. In this process electrical energy is used to cut the material to desire shape and size. Effort is made to utilize the whole energy by applying it to exact spot where the operation needs to be carried out. There is no direct contact between work piece and tool so mechanical pressure does not exist between them. All conductive material can be machined by using EDM irrespective of the hardness or toughness of material. The adequate selection of manufacturing conditions is one of the most important aspects to take into consideration in the diesinking electrical discharge machining (EDM) of conductive Ti 6-4 and electrode is made of copper, as these conditions are the ones that are to determine such important characteristics,material removal rate (MRR). In this paper, a study will be performing on the influence of the factors of pulse on time, pulse off time and power supply voltage. LITERATURE REVIEW Cao and Yang [1] carried out an experiment, and then they have used artificial neural network (ANN) and genetic algorithm (GA) together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been setup to make an equation between input and output parameters. Output parameters such as surface roughness and Material removal rate are then optimized using Genetic algorithm. This model is shown effective and values obtained are much optimized one. Lee et al. [2] have done experiment and found that the results of MRR and surface roughness increases with the values of pulse current but after certain value SR and MRR reduce because of expansion of electric plasma. Surface crack density is affected by pulse current while the crack opening is influenced by the pulse on duration. Dewangan and Prabhkar [3], they work on An experimental study on Electric discharge machining process for machining parameter of AISI P20 tool steel Using U-shaped, A well-designed experimental scheme was used to reduce the total number of experiments and by Taguchi method results were determined to be effective. Karthikeyan et al. [4] knowing the importance of AL-SIC particulate composites and their wide spread application from automotive, aircraft to household appliances. However, these materials are hard to machine because of abrasive nature of their composites. Experiment was done on EZNC EDM machine using 20 mm diameter copper electrode. Three level factorial designs were used and its analysis is done. RESEARCH OBJECTIVE From the literature review it is quite evident; there are many techniques available for optimization of EDM process parameters. The objective of the research to find out optimum value of EDM process parameters that maximize the material removal rate in order to provide the best machining condition. EXPERIMENT & OBSERVATIONS The work piece is Ti 6-4 material and the tool or electrode is made of a copper. Titanium alloys are blend of titanium and other alloying elements. Titanium alloys are known for high tensile strength and toughness even at elevated temperature as All rights Reserved 357

2 International Journal of Advance Engineering and Research Development (IJAERD) as 5000C. Ti 6-4 is very high corrosion resistivity and light in weight. But due to the high cost of raw materials as well as high processing cost their applications are limited to sports cars, sports, military use, aircraft, spacecraft equipment and highly stressed components such as connecting rod. Figure.1: Electric discharge machining set up The followings are the different electrode materials which are used commonly in the industry:- TABLE.1: Properties and composition of Ti 6-4 Properties Melting Point Young Modulus Density Values C Poisson s Ratio 0.18 Aluminum 6% 115 GPa 4420kg/m2 Vanadium 4% Iron 0.25% Oxygen 0.20% Properties Values Melting Point C Young Modulus 121 GPa Density 8960 kg/m2 Poisson s Ratio 0.34 Electrical Resistivity 1.67X10-8Ω-m TABLE.2: Properties and description of copper electrode TABLE.3: Working condition and its description Working Condition Description Copper electrode was used to drill holes in Ti 6-4 block. All the experiments were performed with normal polarity where work piece Discharge Current (A) 10,18,26,36,43,50 acts as a cathode and electrode as anode. Total 30 experiments were Pulse on time (μs) 11,40,50,55,60,95 conducted 6 levels of controlled variables. Input or controlled variable are discharge current (X1), pulse on time (X2) and pulse off time (X3) and their effects of out put or experimental variables material removal Pulse of time (μs) 5,7,9,20,30,40 rate (Y1) and surface roughness (Y2) are observed and recorded. A multiple regression analysis is used to model material removal rate and surface roughness in relation to input parameters mentioned commonly known as objective functions. Statistical validity of the model is tested at 5% level of All rights Reserved 358

3 Genetic Algorithm model (GA):- The objective function can be described as follow. Maximize: objective function1 Y1= constant +a X1 + b X2+ c X3 Y1= Material removal rate (MRR) Variables: X1: Discharge Current X2: Pulse on time X3: Pulse off time a, b, and c are the coefficient of these variables. Constraints: X1>= 10, X2<=50 X2>=10, X2<=250 X3>5, X3<=150 International Journal of Advance Engineering and Research Development (IJAERD) Fitness function is evaluated by the non-dominated solutions which are ranked by value of each objective function from low to high, so a decision maker can take the solutions according to organization objective. Figure.2: flow chart of GA S.No. Discharge Current(A) X1 TABLE.4: Experimental Data Pulse on time X2 Pulse off time MRR(mm 3 /min) Y1 X All rights Reserved 359

4 International Journal of Advance Engineering and Research Development (IJAERD) Regression Analysis: MRR versus A, On, Off DATA ANALYSIS Continuous predictor standardization Levels coded to -1 and +1 Predictor Low High A On Off 5 40 Analysis of Variance Model Summary Coded Coefficients TABLE.5: DF, SS, MS, F, P Values of Regression Method of MRR Source DF Adj SS Adj MS F-Value P-Value Regression A On Off Error Total S R-sq R-sq(adj) R-sq(pred) % 77.68% 69.57% TABLE.6: Coefficient, SE Coefficient, T and P values of MRR All rights Reserved 360

5 Fits and Diagnostics for Unusual Observations Regression Equation in Encoded Units International Journal of Advance Engineering and Research Development (IJAERD) MRR = A On Off Term Coe f SE Coef T- P- VIF Value Value Constant A On Off TABLE.7: Fits and Diagnostics for Unusual Observations R Large residual Std Obs Mrr Fit Resid Resid R Figure.3: MRR vs. A, On and Off Optimizing by Genetic Algorithm Technique This MRR has got R-sq value of 82.46% Though there is lack of fit, being R-sq value 77.68% is taken as the regression equation. These equations are optimized using genetic algorithm in mat lab software tool box Optimization running. Optimization terminated.69 Objective function value: Optimization terminated: average change in the fitness value less than options.tolfun. This is the optimized Material removal rate and it is obtained at for the values 49.44, 95 and of I p, T on, T off respectively. Min and max boundary conditions are [10, 11, 5] and [50, 95, 40]. From the table 7 it can be concluded that Mrr is and fit the error is All rights Reserved 361

6 International Journal of Advance Engineering and Research Development (IJAERD) Figure.4: Mean of MRR vs. A, On and Off CONCLUSION To comprehend the association between material removal rate of the EDM process parameters and its statistical significance, regression analysis has been performed. Three process variables namely discharge current, pulse on time and pulse off time are taken as explanatory variables and material removal rate explained variables. Statistical significance of the model has been assessed at 5% level of significance. From table 4-5-6, it is evident that p value =.001, which is smaller than.05 and also the F value is significantly higher than the calculated value. Hence the proposed regression model is highly significant. The value of adjusted R-square is 77.86%,. The error in model is 4.33%. From the model it can be concluded that discharge current is the most important process parameter which affect MRR, followed by pulse off time(negative effect on MRR) and pulse on time. The outcome confirms that discharge current, pulse on time and pulse off time have major effect on material removal rate and surface roughness. The results of the research divulge that appropriate selection of input parameters will play a important role in Electric Discharge Machining. From the figure 5-7 it can be concluded that. The MRR is increasing with increase in discharge current almost linearly. The MRR is increasing with increase in pulse on time initially at slower rate but later the increase is at a faster rate, The MRR is decreasing with increase in pulse off time almost linearly. REFERENCES Cao, F.G., and Yang, D.Y., 2004, The study of high efficiency and intelligent optimization system in EDM sinking process, Journal of Materials Processing Technology, 149(1-3), pp Lee H.T, Hsu F.C., and Tai T.Y., 2004, Study of surface integrity using the small area EDM process with a Copper-Tungsten electrode, MATERIAL SCIENCE and ENGINEERING, A364, pp [1] Dewangan, S., Datta, S., Patel, S.K., and Mahapatra S.S., 2011, A case study on quality and productivity optimization in electric discharge machining, 14 th International Conference in Advanced Materials and Processing Technologies AMPT July, Istanbul, Turkey. [2] Karthikeyan, R., Lakshmi Narayanan, P.R. and Naagarazan, R.S., 1999, Mathematical modelling for electric discharge machining of aluminium-silicon carbide particulate composites, Journal of Materials Processing Technology, 87(1-3), pp [3] Joshi, S, N., and Pande, S.S., 2011, Intelligent process modelling and optimization of die-sinking electric discharge machining, Elsevier, 11(2), pp [4] Saeed Daneshmand1*, Ehsan Farahmand Kahrizi2, Esmail Abedi3, M. Mir Abdolhosseini4,2013, Influence of Machining Parameters on Electro Discharge Machining of NiTi Shape Memory Alloys, Int. J. Electrochem. Sci., 8 (2013) [5] Vishnu D Asal, Prof.R.I. Patel, Alok B Choudhary / International Journal of Engineering Research and Applications (IJERA) ISSN: Vol. 3, Issue 2, March -April 2013, All rights Reserved 362