Optimization of Cutting Parameter of (SS302) on EDM using Taguchi Method Chintan A. Prajapati 1 Prof. Dr. Prashant Sharma 2 Prof.

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1 IJSRD - International Journal for Scientific Research & Development Vol. 2, Issue 01, 14 ISSN (online): Optimization of tting Parameter of (SS302) on EDM using Taguchi Method Chintan A. Prajapati 1 Prof. Dr. Prashant Sharma 2 Prof. Manish Pokharna 3 1 M.Tech Student 2 Professor 3 Asso. Prof. 1 Production Engineering 2, 3 Mechanical Engg. 1, 2, 3 Pacific University, Udaipur Abstract---In this work, Optimization of operating parameters that are an important step in machining process, particularly for operating unconventional machine like EDM. The objective of this work is to investigate the optimum cutting parameters for a work piece (SS-302) & tool material (Copper, uminum, ass) combination on Electrical Discharge Machine. To produce any product with desired quality by machining process, proper selection of process parameters is must essential for better product. This best parameters combinations can be accomplished by Taguchi optimization approach. The aim of the present work is to investigate the effect of process parameters on Material remove rate (MRR), wear ratio (TWR), Surface roughness (SR) to obtain the optimal setting of this cutting parameter. Key words: Taguchi, Anova, MRR, TWR I. INTRODUCTION Electric-discharge machining (EDM) is a non-conventional, thermo-electric process in which the material from work piece is eroded by a series of discharge sparks between the work and tool electrode immersed in a liquid dielectric medium. These electrical discharges melt and vaporize minute amounts of work material, which are then ejected and flushed away by the dielectric. This technique has been widely used in modern metal working industry for producing complex cavities in dies and moulds, which are otherwise difficult to create by conventional machining. EDM can provide an effective solution for machining hard conductive materials and reproducing complex shapes. EDM involves the phenomena such as: spark initiation, dielectric breakdown, and thermo-mechanical erosion of metals. Metal removal process in EDM is characterized by nonlinear, stochastic and time varying characteristics. Many regression techniques have been used for modeling the EDM process. Unlike milling and drilling operations, operating speeds in EDM are very low. Large electric current discharge can enhance speeds but reduces the dimensional quality of machined surface. Similarly the material removal rate is also affected by other process parameters. [1] Principle of EDM In this process the metal is removing from the work piece due to erosion case by rapidly recurring spark discharge taking place between the tool and work piece. Show the mechanical set up and electrical set up and electrical circuit for electro discharge machining. A thin gap about 0.02mm is maintained between the tool and work piece by a servo system shown in fig 1. Both tool and work piece are submerged in dielectric fluid Kerosene/EDM oil/ demonized water is very common type of liquid dielectric although gaseous dielectrics are also used in certain cases. In the present work the design of experiment (DOE) and Taguchi method is used for optimization of process parameters. The objectives of optimization are (i) maximize the material removal rate (MRR), (ii) minimize the surface roughness value and (iii) maximize the Wear Ratio (TWR). The process parameters considered in experiments include current; gap voltage and material are usually the machining performances to evaluate the machining effect. II. LITERTURE REVIEW 1) Kuriakose and Shunmugam (0) used Genetic gorithms (GA) for solving a multi objective problem in wire EDM process. 2) Kansal et al. (0) adopted the response surface optimization scheme to select the parameters in powder mixed EDM process 3).Keskinetal. (0) used design of experiments (DOE) for the determination of the best machining parameters in EDM. 4) Tzeng and Chen (0) employed a Taguchi fuzzy-based approach for solving the multi-objective optimization problems in high-speed EDM process. ) Mandal et al. (0) have shown the modeling procedure of EDM using neural networks and solution methodology using GA. More recently ) Yuan et al. (0) illustrated the optimization process of highspeed wire EDM process using regression methods. In all the above cases multi-objective formulations are solved for online selection of design variables. III. PROBLEM FORMULATION A. Design Variables The formulation of an optimization problem begins with identifying the underlying design variables, which are primarily varied during the optimization process. In this paper Material, current,, T on and T off are considered as design variables. Be maximized. In this paper, minimization of total machining time is considered as objective function. Fig. 1: Set up of Electric discharge machining [2] l rights reserved by 23

2 Optimization of tting Parameter of (SS302) on EDM using Taguchi Method (IJSRD/Vol. 2/Issue 01/14/13) B. Experimental Equipment and Design An EDM machine, developed by SPARKONIX (I) LTD. was used as the experimental machine. The work material, electrode and the other machining conditions were as follows a) Work piece (anode) : SS-302; LENTH: 0 mm WIDTH: 0 mm THICKNESS: mm Fig. 2: Experimental Work piece Element Weight % C. 0.10% Si 1.00% Mn 2.00% Fe.% Cr % P 0.04% Ni % S 0.030% Table. 1: Chemical Composition of Work Piece) [3] b) Electrode (Cathode) : uminum, ass, Copper LENGTH: 40 mm DIAMETER: 1 mm Material Thermal conductivity(w/m- K) Boiling point (K) Melting point (K) Copper uminum ass Table. 2: Properties of electrodes c) Dielectric fluid A total of five machining parameters ( material,,, and Ton & T off ) were chosen for the controlling factors and each parameter has levels as shown in Table 3. Factor LEVELS Material ALUMINIUM, BRASS, COPPER (amp), 1, Spark 30, 40,0 T on (μs),, T off (μs),, Table. 3: Process pa1rameters and their levels d) Orthogonal Array : A three-level L2is selected for conducting the experiment, because in our consideration we have each 3 level for five factors Material,, and T on & T off Data Sheet SN I V T ON T OFF MRR TWR SR 1 BR BR BR BR BR BR BR BR BR AL AL AL AL AL AL AL AL AL CU CU CU CU CU CU CU CU CU Table. 3 : Process pa1rameters and their levels IV. CALCULATION OF MRR The method used for the calculating the MRR is the weight difference of the work piece Initial and Final machining. The formula used for the Where, W i =Weight before machining (in gm.) W f = Weight after machining (in gm.) = time consumed for machining. (In minutes) The weight of the work piece and tool is taken on the very high precise weighing machine. Least count of the weighing machine is gm. The density of wore piece is gm/mm 3. A. Calculation of tool wear ratio: First we calculate tool wear this formula is give below, Where, T i =Weight of tool before machining (in gm.) T f = Weight of tool after machining (in gm.) ( , , in gm/mm 3 ) t = time consumed for machining. (In minutes) wear ratio is defined as the volume of metal removed from the work piece to the volume of the material loss from the tool. Where, T i =Weight of tool before machining (in gm.) T f = Weight of tool after machining (in gm.) l rights reserved by 24

3 Mean of SN ratios Mean Optimization of tting Parameter of (SS302) on EDM using Taguchi Method (IJSRD/Vol. 2/Issue 01/14/13) ( , , in gm/mm 3 ) t = time consumed for machining. (In minutes) wear ratio is defined as the volume of metal removed from the work piece to the volume of the material loss from the tool.where, TW = wear rate. MRR = Material Remove Rate. B. Solution Methodology: Design of experiments (DOE) was introduced by R.A.Fisher in England in 1 s to study the effect of multiple variables simultaneously. Design of Experiment is an engineering methodology for obtaining product and process conditions, which are minimally sensitive to the various causes of variation, to produce high-quality products with low development and manufacturing costs. Arrangement or patterns for collection of data in a structured manner from the system by imposing treatments for each pattern is called design of experiments (DOE).To infer upon the behavior of a system or process using the experimental data, we must concern ourselves with how to conduct experiments for data collection. This means that the project should be designed such that the affecting parameters can be quickly recognized & their effect on system can be expressed analytically & precisely. For this purpose we need to determine the values of input parameters for the process which will give us the results. Taguchi Method is a new engineering design optimization methodology that improves the quality of existing products and processes and simultaneously reduces their costs very rapidly, with minimum engineering resources and development man-hours. The Taguchi Method achieves this by making the product or process performance "insensitive" to variations in factors such as materials Analyses: Taguchi Orthogonal Array Design L2 Factors: Runs: 2 Columns of L2 (3**13) Array Taguchi Analysis: MRR versus, Material Main Effects Plot for MRR Taguchi Analysis: TWR versus, Response Table for Signal to Noise Ratios Larger is better *log (sum (1/y**2)/n) Material Delta Rank Main Effects Plot for SN ratios of TWR Response Table for Signal to Noise Ratios Larger is better: *log (sum (1/y**2)/n) Material Delta Rank Material Signal-to-noise: Larger is better l rights reserved by 2

4 Mean Mean of SN ratios Mean Optimization of tting Parameter of (SS302) on EDM using Taguchi Method (IJSRD/Vol. 2/Issue 01/14/13) Material Main Effects Plot for TWR Taguchi Analysis: SR versus, Response Table for Signal to Noise Ratios Larger is better *log (sum (1/y**2)/n) Material Delta Rank Material Signal-to-noise: Larger is better Material Main Effects Plot for SN ratios of SR Main Effects Plot for SR General Linear Model: MRR versus, Factor Type s Values Material fixed 3,, fixed 3, 1, fixed 3 30, 40, 0 fixed 3,, fixed 3,, Analysis of Variance for MRR, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Material T On T Off Error Total 2.3 General Linear Model: TWR versus, CURRENT, VOLTAGE, T on & T off Factor Type s Values Material fixed 3,, fixed 3, 1, fixed 3 30, 40, 0 fixed 3,, fixed 3,, Analysis of Variance for TWR, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P Material T On T Off Error General Linear Model: SR versus, CURRENT, VOLTAGE, T on & T off Factor Type s Values Material fixed 3,, fixed 3, 1, fixed 3 30, 40, 0 fixed 3,, fixed 3,, Analysis of Variance for SR, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS Material T On T Off Error Total General Regression Analysis: MRR versus CURRENT, VOLTAGE, TON, TOFF. MRR = POn POff MRR = POn POff F P l rights reserved by 2

5 Optimization of tting Parameter of (SS302) on EDM using Taguchi Method (IJSRD/Vol. 2/Issue 01/14/13) MRR = POn -0.2 POff General Regression Analysis: TWR versus CURRENT, VOLTAGE, TON, TOFF. TWR = POn POff TWR = POn POff TWR = POn POff General Regression Analysis: SR versus CURRENT, VOLTAGE, TON, TOFF. SR = POn POff SR = POn POff SR = POn POff V. CONCLUSION After the Taguchi analysis we have taken a larger is better for the Signal to ratio form equation *log ( (1/y 2 )/n). From main effects plot for MRR, it is concluded that, copper is the best tool material from the three. Following are the optimum parameter combinations are as below 1) For maximum MRR : A current, 30V voltage, μs Pulse on and μs Pulse- off 2) For minimum TWR : Amp, voltage 30V, Pulse on μs and Pulse off μs 3) For minimum SR : A, 0V voltage, μs Pulse on time, μs Pulse off time In general linear model we conclude that the value of P is less than 0.0 then this factor is more significant from MRR, TWR & SR. General Linear Model analysis for MRR P value is 0.00, for TWR P value is 0.0 and for SR P value is 0.00 then we conclude is more significant for MRR & SR and TWR. Elsevier Science & Technology Books, Pub. Date: October 03.) [] FISHER D.O.E, Macmillan publishing society. [] COX D.R, N.REID. The Theory of D.O.E, CHAPMAN & HALL/CRC. [] Rathod K. B., Lalwani D. I. Experimental investigation on optimization of fuzzy logic controlled EDM. (INCAMA-0, 2-2 March-0). [] I. Puertas and C.J. Luis, A study of Optimization of Machining Parameters for Electrical Discharge Machining of Boron Carbide, (Journal of Materials and Manufacturing Processes, Vol. 1, No., pp41-0, 04). [] Y.S.Tarng and W.H. Yang, Application of the Taguchi Method to the Optimization of the Submerged Arc Welding Process, (Journal of Materials and Manufacturing Processes, Vol.13, No.3, pp4-4, 1). [11] H.T. Lee and J.P. Yur, Characteristic Analysis of EDMed Surfaces Using the Taguchi Approach, (Journal of Materials and Manufacturing Processes, Vol.1, No., pp1-0, 00). [12] M. Tsai, P.J. Wang. 01. Semi-empirical model of surface finish on electrical discharge machining. (Int. J. Mach. s Manuf. 41(): 14-14). [13] M.L. Jeswani. 1. Roughness and wear characteristics of spark-eroded surface. Wear. 1: [14] Gary F. Benedict Non-traditional manufacturing process (Marcel Dekker, NEWYORK, U.S.A. 1). [1] Macro Boccadoro, Andrea Buzzini, Stifano Bonini, process parameter optimization in electrical discharge machining. (United states patent, patent no. US 0012 B1. Date: July, 2. 03). [1] Yuji Kaneko, Ichiro Araie, Koji Shu. Method and apparatus for controlling electrical discharge machining (U.S. patent, Patent no: 421, Date: Jun. 2, 1). REFERENCES [1] Rathod K. B., Lalwani D. I. Experimental investigation on optimization of fuzzy logic controlled EDM. (INCAMA-0, 2-2 March-0). [2] [3] [4] DOUGLAS.C.MONTGOMERY, Design and analysis of xperiments.fifth Edition, Arizona State University. [] JIJU ANTONY, Design of experiments for engineer and scientists, ISBN: 00404, (Publisher: l rights reserved by 2