Optimization of Wire EDM Process Parameters of Al 6061/Al 2 O 3 /3%red mud MMC

Transcription

1 Optimization of Wire EDM Process Parameters of Al 6061/Al 2 O 3 /3%red mud MMC Dhinesh kumar.k #1 Sivakumar.M *2, Sivakumar.K *3,Kumar.M *4 # P.G scholar, Department of Mechanical Engineering,Bannari Amman Institute of Technology, Sathyamangalam, India. * Associate Professor, Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India * Professor, Department of mechanical engineering, Bannari Amman Institute of technology, Sathyamangalam, India. * Assistant Professor, Department of mechanical engineering, Bannari Amman Institute of Technology, Sathyamangalam, India. Abstract Wire electric discharge machining (WEDM) is one of the non-conventional machining process. It machines only electrically conducting materials by thermo-electrical process.wedm parameters of Al 6061/Al2O3/ 3%red mud metal matrix composite (MMC) with 15mm thick has been studied experimentally. The process parameters considered are pulse on(t on ), pulse off(t off ), voltage(v), current(i) and wire tension(wt) and the output responses are material removal rate(mrr) and surface roughness(r a ). The relation between the process parameters and the output responses are developed by linear regression analysis. The experiments are conducted based on Taguchi L 27 (3 5 ) orthogonal array. The optimised set of process parameter is obtained using Taguchi technique and confirmation test was conducted. Index terms: Wire Electrical Discharge Machining, Taguchi method, orthogonal Array, MRR, surface roughness and Al 6061 MMC. I-INTRODUCTION Machining is a process of removing raw material from the workpiece. This is also called as subtractive manufacturing process using machine tools. Machining can be used to manufacture parts on material such as metals, wood, plastic, ceramics and composites [1]. Now a days modern machining is carried out by computer numerical control (CNC), in which computers are used to control the movement and operation of machines. Wire EDM was introduced in 1960s' and has modernized the metal manufacturing, die and steel industries. It is probably the most exciting and diversified machine tool developed for industries in the last fifty years and has numerous advantages to offer. In this process a moving wire travels along a recommended path and removes material from the work piece. Wire EDM uses electro-thermal mechanism to cut electrically conducting materials. The material is removed by a series of discrete sparks between the work piece and wire electrode in the presence of dielectric fluid. The area where electric discharge takes place is heated to extremely high temperature, so that the surface is get melted and eroded. The removed particles are flushed away by the flowing dielectric fluid and provide an insulating effect. As the wire electrode and workpiece never make contact there is no cutting force involved during the process. II-LITERATURE REVIEW Mevada [2] conducted experiment on WEDM based on Taguchi L 27 orthogonal array choosing peak current, pulse on time and pulse off time as input parameters and MRR and surface roughness as output responses taking Inconel 600 material machined by Molybdenum, plain brass and zinc coated brass wires as wire electrode. The level of 58 importance and percentage of contribution are determined by using analysis of variance (ANOVA) andregression equations are mathematically modelled and optimized using greyrelational analysis technique was employed. Narender Singh et. al.[3] investigated the effect of WEDM process parameters on Al 10%SiCP MMCwith pulse on time, current and flushing pressure as process parameter and MRR, tool wear rate, taper, radial overcut, and surface roughness as responses using brass wire electrode of diameter 0.27 mm. Taguchi L 27 orthogonal array has been used for experimental design. Mathematical model has been developed by regression analysis and optimized using gray relational analysis technique. Theresults revealed that pulse on is the most significant factor. Suresh kumaret. al. [4]explored the effect of various WEDM of Al 6351 with 0%,5%,10% wt% of B 4 C and 5 wt% of SiCtaking pulse on time, peak current, wire speed as process parameters on surface roughness(r a ) and kerf as performance measuresusing plain brass wire. Input and output relationship has been made by regression analysis and the experiments conducted based on Taguchi L 27 OA.Gray relational analysis was used for optimization. Increase in the pulse on time and the B 4 C particles present in the composite by weight increasesthe kerf widthand B4C particles present in the compositeincreases the surface roughness. Shandilyaet.al.[5] made an attempt on WEDM of 10% SiCp/6061 Al MMC selecting pulse-on-time, pulse-off-time, peak current, and wire feed as process variables on surface roughness. The experiments were carried out based on Box- Behnkendesign with diffused brass wire of diameter 0.25 mm and the input and output relationship were developed using response surface methodology. The optimization was done using genetic algorithm. Kishore et. al. [6] WEDMed the Al %Al2O3 MMC taking pulse-ontime, pulse-

2 off time, voltage, bed speed and currentas process variables on MRR, and surface roughness using molybdenum wire and L18 OA. The mathematical model was developed using multiple linear regression analysis. The optimised results using Taguchi shows that the MRR is the most affected by bed speed and surface roughness is much affected by peak current.satheesh Kumar Reddy et.al.[7] attempted to find out the parameters affecting themrr and surface roughness while WEDM of Al + 3% SiC MMC using Taguchi L18 OA. The significant factors are found using ANOVA and optimised using Taguchi. Singh et.al.[8] attempted to observe the EDM with plain dielectric fluid and Silicon Carbide (SiC) abrasive powder suspended dielectric fluid on surface roughness of stir-cast 6061Al/Al 2 O 3 workpiece. The results of both the processes have been analysed using Lenth's method to find the significant parameters and to obtain the optimum machining parameters. It was found experimentally that abrasive particle size, abrasive particle concentration and pulse current are the most significant parameters that affect the surface characteristics From the above literature it is found only limited work has been attempted on Al 6061/Al2O3/red mud 3% taking pulse-on, pulse-off, current and voltage for measuring material removal rate and surface roughness (R a ) by using Taguchi method. III-EXPERIMENTAL DETAILS The experiment is carried out on SODIACK MARK25-A325 Wire-cut electrical Discharge Machine as shown in figure.1.in this machine X-Y coordinate worktable, U-V auxiliary table, wire spool system, microcomputer control cabinet and liquid dielectric system are the controlling units.the dielectric fluid used in this experiment is dem-mineralised water, circulated by a centrifugal pump, filter, flow control valve andconvergent nozzles. analysis. The specimen size taken for study is mm.the wire electrode material chosen for machining is brass wire of 0.25mm diameterof 15mm thick. TABLE-I CHEMICAL COMPOSITION OF MMC Elements Chemical Composition% Si 0.74 Fe Cu 0.42 Mn 0.07 Mg 0.86 Cr Ni < Zn Ti Ag < B Be < Bi < Ca Cd Co < Li < Na P < Pb Sn Sr < V Zr Al 97.3 B -EXPERIMENTAL DESIGN After performing a pilotstudy, the range of five machining parameters pulse on(t on ), pulse off(t off ), voltage(v), current(i) and wire tension(w t ) the levels has been selected for experimentation. The input parameters and their levels are given in the Table II. Other factors like product shape, work piece height, wire material, wire diameter, angle of cut, work piece material, work piece hardness and length of cut were kept constant during experimentation in order to avoid their effect on the measure of process performance. Fig.1.WEDM A- MATERIAL SELECTION The work piece material selected for the present study is Al 6061/Al 2 O 3 /3% red mud MMC and its chemical composition of isgiven in Table-I after spectrometry 59 The experiments are planned based on the orthogonal array. The experiments were conducted according to Taguchi L 27 orthogonal array (3 5 ) [2, 3, 8] obtained from Minitab software as given in Table III. The Taguchi method apparently has consistency in experimental design, analysis, reduction in time and cost of experiments. The design of experiment was based on L 27 (3 5 ) orthogonal array[2][3]. The process parameters considered for the study are pulse on, pulse off, voltage, current and

3 wire tension. The various levels of control factors in this level of experimental study are as given in table II. TABLE-II LEVELS OF VARIOUS CONTROL FACTORS SYMBOLS MACHINING UNITS LEVEL 1 LEVEL 2 LEVEL 3 PARAMETERS T on Pulse on Time µs T off Pulse off Time µs I Current milli amps V Voltage Volt Wt Wire Tension gms TABLE III L 27 ORTHOGONAL ARRAY S.No. T on T off V I W t

4 The experiments are performed based on the run order of L 27 OA. The time taken for removing (5 5 15mm) square piece of different parameter combinations is noted and themrr is calculated usingequation (1)[10]. MRR = Vc b h (mm3 /min) (1) Where, V c =Cutting length/time taken (mm/min) h =Height of the workpiece (mm) b =2W g +d (mm) W g = Spark gap (mm), d=diameter of the wire (mm) The surface roughness of the machined workpiece is measured via surface roughness tester, SURFTEST SJ- 210 series. The surface roughness parameter chosen in this experimental study is R a. IV-RESULTS AND DISCUSSION Design optimization can be defined as the process of either maximizing functional effect or minimizing undesirable effect. Initially in the present investigation, the Taguchi technique is used to optimize the process variable for better MRR and surface roughness. The S/N ratio measures how the response varies relative to the minimum or maximum value under different noise situations. The relationship between the response and process variablesis developed using linear regression analysis. The results of response measures are converted to S/N ratio using equation (2) & (3) [11], S N = 10 log 1/y2 /n (2) S N = 10 log y2 /n (3) Table-IV shows the experimental outcome ofmean MRR and R a, with signal to noise ratio of MRR and R a and the minimized value of Z calculated from equation (7). The Fig. 2 and Fig. 3 show the response graph and signal to noise graphs of MRR.It shows that MRR increases with the increase in pulse on time, peak current and wire tension. It shows that MRR decreases with the increase in pulse off time. TABLE-IV RESULT OF OUTPUT PARAMETERS Fig.2 Effect of control factors plots for raw data on MRR 61

5 The regression equation for R a is R a = Ton Toff I V Wt (5) S = R-Sq = 92.2% R-Sq(adj) = 90.3% Fig.3 Effect of control factors plots for S/N ratios on MRR. The Fig.4 and Fig.5 show the response graph and signal to noise graphs of R a.it shows that R a decreases with the increase in pulse off time and considerable increase in voltage and wire tension. It shows that R a increases with increase in pulse on time. In the present study main objective is to maximise MRR and minimise R a by using the following equation (6) & (7)[12]. Since, the objective is written for minimization, equation (7) is choosen for further optimization process. (Maximization) Z = (0.5) (Ra /Max. Ra value) + (0.5) (MRR/Max. MRR Value) (6) (Minimization) Z = (0.5) (Ra/Max. Ra value) (0.5) (MRR/Max. MRR Value) (7) V-SELECTION OF OPTIMAL PARAMETER The optimal value is obtained by substituting the mean of R a and MRR values in the minimization equation (8)[14], (Minimization) Z = (0.5) (Ra/3.1) (0.5) (MRR/14.1) (8) Fig.4 Effect of control factors plots of raw data on R a. The optimal value obtained by Taguchi method and that minimum value of Z = , and the corresponding parameter combination for this minimum value is (13T on 14T off 2100I 58 V 160W t ). For this combination,the MRR and R a values are found using the equation (4)and (5). The corresponding MRR and R a values for the above set of parameter combination is mm 3 /min and µm respectively. Fig.5 Effect of control factors plots for S/N ratio on R a. The mathematical model has been developed using linear regression analysis to predict the MRR and R a values RESPONS using equation (4) and (5). ES The regression equation for MRR is MRR = T on T off I V Wt (4) S = R-Sq = 92.1% R-Sq(adj) = 90.2% 62 CONFIRMATION EXPERIMENTS Confirmation experiments are conducted at optimal parameter of MRR and R a. The deviations of predicted results from experimental results are calculated as percentage error [13]. %ERROR= experimental value predicted value experimental value TABLE-V (5) COMPARISON OF EXPERIMENTAL AND OPTIMIZED VALUE EXPERIMENT AL VALUES PREDICTE D VALUES % ERROR MRR Ra VI -CONCLUSION

6 The experiment conducted as per L 27 OA and their result is used to develop a linear regression model. The investigation results of WEDM of Al 6061/Al2O3/3%red mud shows that MRR decreases with the increase in pulse off time and voltage whereas R a decreases with the increase in pulse off time. Taguchi optimization technique is used for optimization and the variation between the experimental values and predicted values are within good agreement which ensured by the confirmation test result. REFERENCES [1] P.K.Mishra, Unconventional machining process, Narosa publishing house pp [11] Ross, P.J, Taguchi Techniques for quality Engineering, McGrew-Hill, Second Edition (1996)209. [12] Saravanan, R 2006, Manufacturing Optimization Through Intelligent Techniques, Isted, CRC Press, Boca Raton, FL. [13] VikramReddy.V, Madarvalli.P, Sridhar Reddy.C.H Electrical Discharge Machining of PH17-4 Stainless Steel using Surfactant and GraphitePowder mixed dielectric Advancesin Materials, Manufacturing and Applications, ISBN , April [14]Sivakumar, M, Sivakumar, K, Shanmugaprakash, R, Vignesh, S Parameter Optimization of Wire Electric Discharge Machining on AISI D3 Steel with different Thickness, International Journal of Applied Engineering Research, vol.10, no. 62, pp [2] Mevada J.R A Comparative Experimental Investigation on Process Parameters Using Molybdenum, Brass and Zinc-Coated Wires in Wire cut EDM International Journal of Scientific & Engineering Research, Volume 4, Issue 7, ISSN , July [3] Narender Singh P, Raghukandan k, Pai B.C Optimization by Grey relational analysis of EDM parameters on machining Al 10%SiCP composites Journal of Materials Processing Technology , ISSN , 2004 [4] Suresh Kumar S, Uthayakumar M, ThirumalaiKumaran S, Parametric optimization of wire electrical discharge machining on aluminium based composites through grey relational analysis Journal of Manufacturing [5] Shandilya p and Jain P genetic algorithm based optimization during wire electric discharge machining of metal matrix composite Annals & Proceedings of DAAAM International Volume 23, No.1, ISSN , 2012 [6] Kishore G.C, ArunaDevi.M and Prakash C.P.S parametric Optimization of Wire Electrical Discharge Machining by Taguchi Technique on Composite Material International Journal of Engineering Research &Technology,Vol. 4, Issue 09,ISSN: , Sep [7] Satheesh Kumar Reddy K and Ramesh S Parametric Optimization of Wire Electrical Discharge Machining of Composite Material International Journal of Advanced Research in Computer Engineering & Technology, Volume 1, Issue 3, ISSN: ,May [8]Shankar Singh, SachinMaheshwari&Poorn Chandra Pandey 2008, Effect of SiC powder suspended dielectric fluid on the surface finish of 6061Al/Al 2 O 3P /20p composites during electric discharge machining, International Journal of Machining and Machinability of Materials, vol. 4, no. 2-3, pp [9] Vamsi Krishna Pasam, SurendraBabuBattula, MadarValli, P &Swapna, M 2010, Optimizing surface finish in WEDM using the Taguchi parameter design method, Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 32, no. 2, pp [10] Ravindranadhbobbili, Madhu V, Gogia A.K. An experimental investigation of wire electrical discharge machining of hot-pressed boron carbide Defence Technology 11, ISSN , July