PARAMETRIC OPTIMIZATION OF Al-SiC12% METAL MATRIX COMPOSITE MACHINING BY ELECTRICAL DISCHARGE MACHINE

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1 5 th International & 6 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 014) December 1 th 14 th, 014, IIT Guwahati, Assam, India PARAMETRIC OPTIMIZATION OF Al-SiC1% METAL MATRIX COMPOSITE MACHINING BY ELECTRICAL DISCHARGE MACHINE Rajesh Kumar Bhuyan, B.C.Routara*, Arun Kumar Parida, A.K.Sahoo School of Mechanical Engineering, KIIT University, Bhubaneswar, rajesh_bhuyan001@rediffmail.com,bcroutray@gmail.com, arunkumar_parida@yahoo.com, ashok_lala@rediffmail.com Abstract In the present study, pure aluminium alloy is mixed with 1% SiC on weight basis to fabricate the metal matrix composite (MMC) material. The objective of this paper is to investigate the effect of process parameters such as pulse on time(t on ), peak current (I p ) and flushing pressure (F p ) on metal removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) during electrical discharge machining (EDM) of Al-SiC1% MMC.The experiment is followed by Central composite design (CCD) method under different combination of process parameters.in this paper Response surface methodology (RSM) is used to develop the mathematical model and to correlate the process parameters with the response. Also Analysis of Variance (ANOVA) technique is used to check the significance of the model and confirmation test is conducted to compare the experimental data with the predicted data to identify the effectiveness of the proposed method. From the main effective plot it is found that peak current is the most significant parameter among the other. Further the morphology study of the composite is carried out by the Field emission Scanning electron microscope (FESEM) of the surface after machining to make a relation with the model. Keywords: Aluminium metal matrix composite, ANOVA, RSM, FESEM 1 Introduction Now a day s, researcher all over the globe are focusing mainly variety of innovative composite materials which are quickly replacing conventional materials for different applications like automotive, aerospace, defence, sports, Electrical appliance and other industries. According to Hung et al. (1994) Aluminium alloys are light weight in compare to monolithic material when it is reinforced with SiC to form an aluminium metal matrix composite. Renjie et al. (01) expressed their view that Al-SiC composite is one of the advance composite material that posses superior physical and mechanical properties in compare to other conventional material. Kohli et al.(01) have applied fuzzy logic analysis to compare the experimental results with the result generated by fuzzy model during die sinking EDM process using copper as electrode and Medium Carbon Steel (AISI 1040) as work-piece to evaluate the material removal rate. Hocheng et al. (1997) studied the effect of machining parameters like electrical current, pulse on time in EDM of aluminium silicon carbide (Al-SiC) composite and compared the crater size produced by a single spark. According to them, the crater size of the Al-SiC composite is larger than common steel and the material removal rate is proportional to the applied current and pulse on time. Muller et al. (001) investigated the machinability of AA618-SiC-0P and A356-SiC-35P composite material in machining by both EDM and LASER process. They stated that MRR decreases with increase in percentage of SiC, more ever LASER machining induced higher thermal damage to the surface in compare to EDM. Patel et al. (009) studied about machining characteristics, surface integrity and material removal mechanism of Al O 3 SiCw TiC ceramic composite and found that surface roughness increases with discharge current and pulse on time as well as it provides the key information on the mechanisms of formation of recast layer. However, Dhar et al. (007) conducted an experiment, in which pulse current, pulse duration and gap voltage are selected as process parameters that affect metal removal rate, tool wear rate,radial over cut and surface roughness of Al 4Cu 6Si alloy with 10 wt.% SiC composites. They observed that material removal rate, tool wear rate and radial over cut increase significantly in a nonlinear fashion with current. Also a mathematical model is established to predict the optimal conditions suitable for the selected model. Karthikeyan et al. (1999) developed a mathematical model for the response characteristics like metal removal rate, tool wear rate and surface roughness using the process parameters such as current, pulse duration and the percent volume fraction of SiC followed by three level full factorial design and analyzed by ANOVA. They concluded that material removal rate decreases with increase in the percentage volume of SiC, while the tool wear rate and the surface roughness increases with an increase in percentage volume of SiC. Singh (01) 345-1

2 PARAMETRIC OPTIMIZATION OF Al-SiC1% METAL MATRIX COMPOSITE MACHINING BY ELECTRICAL DISCHARGE MACHINE used L 18 orthogonal array and grey relational analysis to investigate the effects of pulse current, pulse on time, duty cycle, gap voltage and tool electrode lift time for the responses like material removal rate, tool wear rate and surface roughness during the EDM process with 6061Al/AlO3p/0P composites. According to him pulse current is the most effective parameter among the other parameters. Gopalakanan et al. (013) studied the hybrid metal matrix composite using Taguchi based grey relation analysis and established a correlation between process parameters such as pulse on time, current, gap voltage and pulse off time with material removal rate, surface roughness, electrode wear ratio and according to them pulse on time and pulse current are the most significant parameters. Shandilya et al. (01) used Response surface methodology and ANOVA technique to optimize the process parameters of Al6061-SiC metal matrix composite during WEDM and studied the microstructure of the machining surface through scanning electron microscopic. Hu. et al. (013) Compared the effect of conventional EDM and Powdermixed EDM on the Al-SiC composite and found that roughness decreases about 31.5% during powder-mixed EDM. Based on the above consideration, limited work is carried out with pure Aluminium alloy-sic on electric discharge machining. The objective of this paper is to study the effects of peak current, pulse on time and flushing pressure on the metal removal rate, tool wear rate and surface roughness using a Response surface methodology experimental technique. The second order mathematical model is developed based on response surface methodology which then after validated by using confirmation test. Experimental Details.1 Specimen Preparation The work piece materials are fabricated by using commercial pure aluminium with purity 98% and silicon carbide having the average particle grain size is 0.08 mm. The composite materials are prepared on the basis of 1% weight fraction of SiC and remaining weight as aluminium alloy by using Stir casting method. In this process the molten aluminium and SiC are aggregated at 400 rpm to get uniformly distributed of the SiC in aluminium to obtain the wok piece material.. Experimental setup and procedure The experiments are conducted at Central Institute of Plastic Engineering And Technology, Patia, Bhubaneswar using the Electrical Discharge machining as shown in Figure 1 with a model of MIC-43CS CNC manufactured by ECOWIN EDM. The work pieces are prepared 40 mm in diameter with 10 mm thickness and the tools are made of electrolyte copper electrode with average diameter 5.4 mm for each experiment. The dielectric medium used is commercial EDM oil with side impulse jet flushing system adopted to flush away the eroded material from both work piece and tool near the sparking zone. The experiments are conducted based on the central composite design method and each work piece is machined up to depth mm. The machining time is recorded in the timer of EDM machine. The Material removal rate, tool wear rate are measured by using weight difference between the work piece and tool by using electronic METLERPM00 digital weight measurement machine. The surface roughness is measured by MITUTOYO surface roughness tester. Figure1 Experimental set up of EDM.3 Response Variables Evaluation The machining performances are evaluated to study the effect of MRR, TWR and SR on Al-1%SiC metal matrix composite with the following numerical formula. Material removal rate (MRR):- Material removal rate is defined as ratio of the differences in weight of the work piece before and after machining to machining time. Mathematically it is expressed as Wb Wa MRR = mg / min (1) t where W = Weight of work piece before machining (mg) b W a = Weight of work piece after machining (mg) t = Machining time in (min) Tool wear rate (TWR):- Tool wear rate is defined as ratio of the differences in weight of the tool before and after machining to machining time. Mathematically it is expressed as Tb Ta TWR = mg / min () t 345-

3 5 th International & 6 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 014) December 1 th 14 th, 014, IIT Guwahati, Assam, India where T = Weight of work piece before machining (mg) b T a = Weight of work piece after machining (mg) t = Machining time (min) Surface roughness (SR):-The R a values are taken at three different position of the work piece and the average of three measurements is recorded..4 Design of experiment The experiments are conducted based on the effect of three process parameters like peak current (I p ) in Ampere (A), Pulse on time (T on ) in Microsecond (µs), Flushing pressure (F p ) in Kg/cm proposed by Central composite design (CCD) method to run 0 experiments. The process variables with their actual values on different levels are shown in the Table 1. Table 1 Process parameters and their levels Symbols Levels I p T on F p Results and Discussion 3.1 Analysis of Variance (ANOVA) The results are further analyzed with Analysis of variance (ANOVA) and the F-ratio test is performed to check the adequacy of the model.the results of the ANOVA for MRR, TWR and R a are shown in Tables, 3 and 4 respectively. From the ANOVA table it is again analysed that significance level of α=0.05, i.e. for the confidence level of 95%.It has been acceptable that P value less than 0.05 is indicated that the performance of the parameter statistically significant and more than 0.05 less significant to the model. Also it is found that the R value for the MRR, TWR and R a are 0.98, 0.96 and 0.98 respectively that significant the R value is closed to 1 which is desirable. The predicted R is in reasonable agreement with the adjusted R. 3.. Second order Mathematical Model The empirical mathematical relation of the response parameters are defined in terms of process parameter. In this case the model is quadratic in nature involving linear and quadratic interactions of process variables.the mathematical model equation commonly represented by a function (Ø) i.e. R=Ø (T on, I p, F p ) where R is defined as the response. T on is representing pulse on time I p is represent the peak current F p is representing dielectric flushing pressure The mathematical model equation of MRR, TWR and R a are given in equations 3, 4 and 5 respectively. Table Analysis of Variance for MRR Seq Adj Source DF F p SS MS T on I p F p T on *T on I p *I p F p *F p T on *I p T on *F p Total Source R-Sq = 98.61%, R-Sq (adj) = 97.36% Table 3 Analysis of Variance for TWR DF Seq SS Adj MS T on I p F p T on *T on I p *I p F p *F p T on *I p T on *F p I p *F p Residual Lack-of- Fit Pure I p *F p Residual Lack-of- Fit Pure Total R-Sq = 96.9%, R-Sq (adj) = 94.15% F P 345-3

4 PARAMETRIC OPTIMIZATION OF Al-SiC1% METAL MATRIX COMPOSITE MACHINING BY ELECTRICAL DISCHARGE MACHINE MRR Fp Fp = Ton Ton*Fp Ton Ton Ton*Ip Ip Ip Ip*Fp Ton * Fp Ip * Fp Fp Ip TWR = Ton 0.834Ip Fp R a 0.40Ip * Fp = Ton Ip Fp Ton Fp Ip Ton * Ip 0.007Ton * Ip Ton * Fp Table 4 Analysis of Variance for R a (3) Source DF Seq SS Adj MS F P T on (4) (5) I p F p T on *T on I p *I p F p *F p T on *I p T on *F p I p *F p Residual Lack-of- Fit Pure Total R-Sq = 98.17%, R-Sq (adj) = 96.5% 3.3 Interpretation of Plots The main effects plot is most useful when several factors are considered for optimization of the model. The plot show the variation of individual response with the three parameters, i.e. peak current, Pulse on time and Flushing pressure separately As shown in Figure, it is found that with the increase in all the machining parameters the material removal rate is gradually increases. The MRR increases with peak current because at the time of machining, a high amount of discharge current flows in between the work piece and tool. These spark discharges develop impulsive force in the spark gap so that the melting and evaporation occurs and the entire surface is eroded. Also further analysed that when pulse on time increases, the spark discharges for a long time as a result the MRR gradually increases and increase in flushing pressures the wear debris are eliminated quickly in the sparking gaps for which the ionisation of dielectric fluid is continuously replaced in every spark discharge, hence the MRR is increases In Figure 3, it is shown that the tool wear rate increases with peak current but decreases with other parameters like pulse on time and flushing pressure. According to Velusamy et al. (011) when the peak current increases the material is lost on both work piece and tool because the sparking occurs near about 0,000 to 30, 0000 times per second on the machined surface. Therefore with increase of peak current, the spark discharge is also increases which enhanced the TWR. But there is a variation of attribute due to longer spark generation time. The peak current heats the interface surface continuously so that the wear debris are burnt to form the carbon particles and deposited on the tool surface which helps to reduce the tool wear rate. Further higher the flushing pressure, the cooling rate of the tool is increased for which the tool wear rate is reduced. Furthermore as shown in Figure 4, it is found that the surface roughness R a increases with all the process parameters. The increase in current, the MRR becomes faster which indicates that R a increases with the increase of current. However pulse on time is directly proportional to the MRR. Therefore increase in pulse on time the roughness increases. It is also observed that when flushing pressure increases the spark arcing decreases so that MRR increases. Hence rise in flushing pressure, the roughness increases. Figure Main effect plot of MRR 345-4

5 5 th International & 6 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 014) December 1 th 14 th, 014, IIT Guwahati, Assam, India pressure increase more cracks are over the machined surface lowering the pressure cracks will be less. Moreover it has been found that when the current is minimum the spark discharge strikes less on the surface but in the case of maximum it strikes the surface more intensely as a result the diameter and depth of the craters in machining surface increases and the surface become more roughness. Figure 3 Main effect plot of TWR Figure 5 FESEM of machined surface Figure 4 Main effect plot of R a 4 Micro structural studies In Figure 5, it is shows the micro surface textures of the erode surfaces during EDM, process there are a large number of voids, micro holes, gas bubbles and micro crackers appearing on the surface. According to Velusamy et al. (011) the morphological defects are due to high tensile stress developed on the imperfect joining of molten droplets during machining. Due to this a lot of stress gradient exceeds the ultimate tensile strength of the composite material. But Muller et al. (001) explain that during the machining process, there is extremely high energy discharge in the area between work piece and the tool to form a plasma phase. In this phase temperature rises highly so that a molten metal stage is developed on the machining surface.when this machining surface is contacts, with the air and flushing of dielectric result to form the recast layer on the surface. According to Velmurugan et al. (011) flushing pressure plays an important role for this type of morphological defects which depends upon the rate of solidifications on the molten surface. When the flushing 5 Confirmation test The experiments are further analyzed by conducting with a new set of process parameters within the range of previously selective process variables.the predicted result is found by using the mathematical equation 3 to 5 and compared with the experimental observation value. It is found that the error of MRR, TWR and R a within a range of to 3.01 %, 1.5 to.5 % and.1 to 5.41 % as shown in the Table 5, 6 and7 respectively. From this confirmation test it found that the average accuracy of the model is near about 95%. The confirmation test results are calculated using the following formula i.e. (Experimental result - Predicted result) % = 100 Predicted result Table 5 Confirmation test for MRR Test T on I p F p MRR Pred MRR Exp (%)

6 PARAMETRIC OPTIMIZATION OF Al-SiC1% METAL MATRIX COMPOSITE MACHINING BY ELECTRICAL DISCHARGE MACHINE Table 6 Confirmation test for TWR Test T on I p F p TWR Pred TWR Exp (%) Table 7 Confirmation test for R a Test T on I p F p R a Pred R a Exp (%) Conclusion It is conclude that through this analysis Al-SiC 1% composite to be machined using EDM with selecting optimum levels of process parameter that is peak current (I p ), pulse on time (T on ), and flushing pressure (F p ).The parameter peak current is the most significant parameter and it increases linearly with the MRR, TWR and R a.the pulse on time and flushing pressure increase with MRR and R a but decrease with TWR. The developed mathematical model is used to predict the optimal condition suitable for the selected process. ReferencesL Dhar.S, Purohit.R, Saini. N,Sharma.A, Kumar.H (007),Mathematical modelling of electric discharge machining of cast Al 4Cu 6Si alloy 10 wt.% SiC composites, Journal of Materials Processing Technology,Vol. 194, PP 4 9. Henochg.H, LeiW.T,Hsu H (1997), Preliminary Study Of Material Removal In Electrical-Discharge Machining Of SiC-Al, Journal of Materials Processing Technology, Vol. 63,pp Hu.F.Q, Cao F.Y., Song B.Y, HouP.J, Zhang Y, Chen K., Wei J.Q, ( 013), Surface properties of SiCp/Al composite by powder-mixed EDM, Procedia CIRP, Vol Hung N.P, Yang L.J, Leong K.W (1994), Electrical discharge machining of cast metal matrix composites,journal of Materials Processing Technology, Vol.44,pp9-36. Ji.Renjie, Liu.Y Zhang.Y, Cai. B, Ma.J, Li.Xi (01), Influence of dielectric and machining parameters on the process performance for electric discharge milling of SiC ceramic, Int J AdvManuf Technology Vol.59, pp Kohil. A, Wadhwa.A, Virmani.T,Jain.U (01), Optimization of Material Removal Rate in Electrical Discharge Machining Using Fuzzy Logic, World Academy of Science, Engineering and Technology,Vol Muller. F, Monaghan.j (001), Non-conventional machining of particle reinforced metal matrix composites, Journal of Materials Processing Technology, Vol.118, pp Patel K.M, Pandey.P.M,Rao V (009),Surface integrity and material removal mechanisms associated with the EDM of AlO3 ceramic composite, Int. Journal of Refractory Metals & Hard Materials,Vol 7,pp Gopalakanan.S, Ranganathan.S,Senthilvelan.T (013),Statistical optimization Of EDM parameter machining of aluminium hybrid MMC by applying Taguchi based grey analysis, Journal of scientific and Industrial research, Vol. 7, pp Shandilya.P,.Jain P.K. Jain. N.K. (01), Parametric optimization during wire electrical discharge machining using response surface methodology, Procedia Engineering, Vol.38, pp Singh.S (01), Optimization of machining characteristics in electric discharge machining of 6061Al/AlO3p/0P composites by grey relational analysis, Int J AdvManufTechnol,Vol63, pp R. Karthikeyan, Lakshmi Narayanan.P.R, R Naagarazan R.S (1999), Mathematical modelling for electric discharge machining of aluminium silicon carbide particulate composites,journal of Materials Processing Technology, Vol.87,pp Velmurugan.C,Subramanian,R,Thriugnanam,S,Ananada vel.b (011), Experimental investigations on machining characteristics of Al6061 hybrid metal matrix composite process by electrical discharge machining,international Journal of Engineering, Science and Technology, Vol. 3, No. 8, pp VelusamySenthilkumar, Omprakash. B. U (011), Effect of Titanium Carbide particle addition in the aluminium composite on EDM process parameters Journal of Manufacturing Processes, Vol.13,