MULTI-OBJECTIVE OPTIMIZATION OF SPARK EDM PROCESS PARAMETERS FOR INCONEL 800

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1 IJMTES International Journal of Modern Trends in Engineering and Science ISSN: MULTI-OBJECTIVE OPTIMIZATION OF SPARK EDM PROCESS PARAMETERS FOR INCONEL 800 Rajiv kumar.n, Karthikeyan.S, Matheswaran.M.M 3 (Department of Mechanical Engineering, Sri Guru Institute of Technology, Coimbatore, India, rajivj486@gmail.com) (Department of Mechanical Engineering, Sasurie college of Engineering, Tirupur, India, karthismech05@gmail.com) 3 (Department of Mechanical Engineering, Jansons Institute of Technology, Coimbatore, India, madhume0@gmail.com) Abstract This research work deals the performance characteristics in machining of Inconel 800 by Electrical Discharge Machining process. The experiments were carried out as per Taguchi s L8 orthogonal array with each experiment performed under the various machining parameters, namely peak current(x ), pulse- on time(x ) and pulse-off time(x 3 ). They were optimized with considerations of multiple performance characteristics including surface roughness (Ra), electrode wear ratio and material removal rate. The development of regression equation is used to analyze the performance characteristics and the study based on L8 orthogonal array with modified Taguchi method. An orthogonal array, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were employed to study the performance characteristics. Keywords EDM, MRR, EWR, SR, Taguchi, ANOVA, L8 Orthogonal array. INTRODUCTION EDM is a non-traditional method of removing material by a series of rapidly recurring electric arcing discharges between an electrode (the cutting tool) and the work piece, in the presence of an energetic field. The EDM cutting tool is guided along the desired path very close to the work but it does not touch the work piece. Consecutive sparks produce a series of micro-craters on the work piece and remove material along the cutting path by melting and vaporization. The particles are washed away by the continuously flushing dielectric fluid. The material removal procedure can be explained by extremely quick heating, melting and vaporization. The process is based on melting temperature, not hardness, so some very hard materials can be machined this way. The arc that jumps heats the metal, and about to 0% of the molten metal goes in to the fluid. The melted recast layer is about to 30 micro meters thick, and is generally hard and rough. The sinking concept uses the above described physical phenomenon. The tool (anode) will be negatively formed in the work piece (cathode) by the process of sinking caused by the temperature as a result of the high voltage energy. The use of light, thin and compact mechanical elements has recently become a global trend. The search for new, lightweight material with greater strength and toughness has led to the development of new generation of materials, although their properties may create major challenges during machining operations. Having greater hardness and reinforcement strength, these materials are difficult to machine by the traditional methods. Inconel is a registered trademark of Special Metals Corporation that refers to a family of austenitic nickel-chromium-based super alloys. Inconel alloys are typically used in high temperature applications. It is often referred to in English as "Inco" (or occasionally "Inconel"). Common trade names for Inconel include: Inconel 65, Chronin 65, Altemp 65, Haynes 65, Nickelvac 65 and Nicrofer DESIGN OF EXPERIMENTS A. Selecting an electrode When selecting an electrode and its fabrication, these factors need to be evaluated: Cost of electrode material. Ease of difficulty of making an electrode. Type of finish desired. Amount of electrode wear. Number of electrodes required to finish the job. Type of electrode best suited for the work. B. Experimental plan The essential steps include identifying the factors that are to be included in the study and determining the factor levels. It was decided to study the effect of the parameters viz., Peak Current (Amps), Pulse on Time and Pulse off Time on the responses viz. Electrode wear ratio, Material removal rate and surface roughness. Different settings of Peak Current, Pulse on Time and Pulse off Time used in the experiments are summarized in Table.. TABLE. MACHINING PARAMETERS AND THEIR LEVELS SYMBOL MACHINING UNIT L L L3 PARAMETERS A Peak current Amps B Pulse on Time µs C Pulse off Time µs Volume: 03 Issue:

2 IJMTES International Journal of Modern Trends in Engineering and Science ISSN: Experiments are performed by investigators in virtually all fields of inquiry. Usually to discover something about a particular process or system. More formally we can define an experiment as a test or series of tests in which purposeful changes are made to the input variables of a process or system, so that we may observe and identify the reasons for changes in the output response. We can usually visualize the process as the combination of machines, methods. Some of the process variables X, X, X3 Xp are controllable, whereas other variables Z, Z, Z3,.Zp are uncontrollable. C. Orthogonal array Experiment In this work, an L 8 orthogonal array with four columns and eighteen rows was used. This array has eight degrees of freedom and it can handle three-level design parameters. Each machining parameter is assigned to a column, nine machining-parameter combinations being available. The experimental layout for the three machining parameters using the L 8 orthogonal array is shown in Table.. Experimental design using L8 orthogonal array is shown in table METHODOLOGY A. Materials and process Basically, material removal rate, Electrode wear ratio and surface roughness are strongly correlated with machining parameters such as Peak Current, Pulse on Time and Pulse off Time. Proper selection of the machining parameters can obtain a higher MRR, lower EWR and better surface roughness. The EDM test was conducted in ELECTRONICA Electric Discharge machine, having a maximum of 5 Amps current. Work piece is connected with positive terminal and Electrode is connected with negative terminal of the D.C power supply. EDM oil was used as dielectric fluid with pressure of 0.kg/cm, side flushing technique was used to conduct all the experiments. Figure 3. shows the view of work piece and copper electrode. TABLE. Experimental layout using L 8 orthogonal array MACHINING PARAMETER LEVELS A B C TABLE.3 Experimental design using L 8 orthogonal array Peak current (amps) Pulse on time Pulse off Time Fig 3. View of work piece and copper electrode WORK PIECE INCONEL 800 (LxWxT) = (58x30x9)mm ELECTRODE COPPER (LxWxT) = (50X0X0)mm Figure 3. shows a typical EDM control system. Volume: 03 Issue:

3 IJMTES International Journal of Modern Trends in Engineering and Science ISSN: Table 3. illustrates about the composition of various properties of Inconel 800. Table 3. Material Properties of Inconel 800 Fig 3. EDM CONTROL SYSTEM The work piece machining in the EDM is carried as illustrated in figure 3.3 Composition Report (in percentage) Carbon 0.07 Silicon 0.38 Manganese Sulphur 0.0 Phosphorous 0.08 Chromium Nickel Molybdenum 0.9 Copper Aluminium 0.45 Titanium 0.77 Vanadium 0.06 Cobalt 0.0 Niobium 0.06 Ferrous B. Machining performance measure MATERIAL REMOVAL WEIGHT Material removal weight = Volume of metal removal x density of work material Density of work material = 7.95g/cm³ = 7.95x0 ⁶Kg/mm³. Volume of metal removal = Area of slot x Depth of cut Table 3. illustrates the material removal weight for Inconel 800. Table 3. MRW for Inconel 800 Fig 3.3 Work piece Machining The measurement of work piece is shown in figure 3.4 Depth of cut (mm) Volume of metal removal (mm 3 ) Material Removal Weight (kg) MATERIAL REMOVAL RATE MRR = (MRW/T) in Kg/min MRW = Material removal weight ; T = Machining time Fig 3.4 Measurement of work piece Volume: 03 Issue:

4 IJMTES International Journal of Modern Trends in Engineering and Science ISSN: The material removal rate for Inconel 800 is calculated and tabulated as shown in table 3.3 Table 3.3 MRR for Inconel 800 Machining Time (min) MRR (Kg/min) ELECTRODE WEAR RATIO ERW = Weight Before Machining Weight After Machining The Electrode Removal weight for copper is tabulated as shown in table 3.4 Table 3.4 ERW for copper Weight before machining (A) kg Weight after machining (B) kg ERW (A-B) kg EWR (%) = (ERW/MRW) x 00 The Electrode wear Ratio values are calculated and tabulated as shown in table 3.5. Table 3.5 Electrode Wear Ratio ERW (kg) MRW (kg) EWR% SURFACE ROUGHNESS The surface roughness is measured for each trials and is tabulated as shown in table 3.6. Table 3.6 Surface Roughness for Inconel 800 PEAK CURRENT (amps) PULSE ON TIME PULSE OFF TIME ANALYSIS SURFACE ROUGHNESS (Ra) A. Analysis of SN Ratio The modified Taguchi method uses S/N ratio instead of the average value to infer the trial results data into a value for the evaluation characteristics in the optimum setting analysis. This is because S/N ratio can reflect both average and variation of the quality characteristics. In the present work, the optimization of EDM process parameters using Taguchi s robust design methodology with multiple characteristics is proposed. The S/N ratio associated with all the responses, are given below η = 0log 0 ( Ra ) () η = 0log0 MMR ( ) η = 0 EWR log 0 B. Utility concept The utility concept employs the weighing factors to each of the signal-to-noise ratio of the responses to obtain a multi response signal-to-noise ratio for each trial of the orthogonal array. The multi-response signal to noise ratio is calculated by the given equation. () (3) Volume: 03 Issue:

5 IJMTES International Journal of Modern Trends in Engineering and Science ISSN: η = w η + wη w η Where w, w and w 3 are the weighting factors associated with the S/N ratio of each of the responses MRR, EWR, Ra respectively. The overall mean of η associated with 8 trials is computed as (4) = 8 k k= m = η k 8 (5) The effect of a factor level i for parameter j is m = L L ( ηi ) i= (6) The predicted optimum value of signal to noise ratio (η opt ) can be determined as η opt = m + j P [ ( mi, j ) m] max j= (7) C. Determination of optimal EDM process parameters The S/N Ratio for Material Removal Rate, Surface roughness and Electrode wear ratio can be expressed from equations, & 3 and the experimental results of material removal rate & surface roughness for Inconel 800 & EWR for copper and their S/N ratio are tabulated as shown in table 4.. Table 4. Experimental results for MRR, Surface roughness for Inconel 800 & EWR for copper and their S/N ratio The multi-response signal to noise ratio is calculated by the given equation. 3 3 η + wη w η η = w + Where w, w and w 3 are the weighting factors associated with the S/N ratio of each of the responses MRR, EWR, Ra respectively. The multi S/N/ ratio for the experiments are tabulated as shown in table 4. Table 4. Multi S/N ratio (MRR, EWR, Ra) for the experiments for Copper as electrode Multi S/N Ratio (Mn) S/N RATIO (MRR) S/N RATIO (Ra) S/N RATIO (EWR) Volume: 03 Issue:

6 IJMTES International Journal of Modern Trends in Engineering and Science ISSN: Table 4.3 illustrates the mean effective response table for multi-response S/N ratio for copper as electrode. TABLE 4.3 MEAN EFFECTIVE RESPONSE TABLE Machining parameters LEVEL LEVEL LEVEL 3 MAX- MIN Peak current Pulse on time Pulse off time Total mean multi-response S/N ratio =( ) =.3948dB ANALYSIS OF VARIANCE The purpose of the ANOVA is to investigate which EDM process parameters significantly affect the performance characteristic. This is accomplished by separating the total variability of the grey relational grades, which is measured by the sum of the squared deviations from the total mean of the grey relational grade, into contributions by each EDM process parameter and the error. The percentage contribution by each of the process parameter in the total sum of the squared deviations can be used to evaluate the importance of the process parameter change on the performance characteristic. Mean of Multi S/N ratio (M) = Sum of Multi S/N ratio / 8 Results of the analysis of variance for multi performance characteristics is tabulated as shown in table 4.4 TABLE 4.4 RESULTS OF ANALYSIS OF VARIANCE FOR MULTIPLE PERFORMANCE CHARACTERISTICS FOR COPPER AS ELECTRODE SYMBOL MACHINING PARAMETER DF SUM OF SQUARES % C A Peak current % B Pulse on Time % C Pulse off Time % D Error % E Total % 5. CONCLUSION Considering the Table 5. the optimal machining parameters are obtained at third position (A3B3C3) in the machining parameter levels i.e. (Peak current, Pulse ON time, Pulse OFF time). Table 5. explains about the experimental layout using an L 8 Orthogonal array for optimal machining parameters. Table 5. Experimental layout for optimal machining parameters MACHINING PARAMETER LEVEL A B C Table 5. shows the experimental design using L 8 orthogonal array for copper as electrode. Table 5. Experimental design for optimal machining parameters MACHINING PARAMETER LEVEL Peak current (amps) Pulse on Time Pulse off Time The results of initial and optimal electrical discharge machining performance for copper as electrode are tabulated as shown in table 5.3. Table 5.3 Results of initial and optimal EDM performance OPTIMAL MACHINING FACTORS PARAMETERS EXPERIMENT LEVEL A3B3C3 Material Removal Rate (MRR) in (Kg/min) Surface roughness (Ra) in (μm) 3.7 Electrode wear ratio in (%) Multi-response S/N ratio A study has been carried out to optimize the EDM parameters of copper. The conclusions of the experimental work were summarized as follows..the optimal EDM parameters for multiple performance characteristics for graphite as Electrode are: Peak current 4 Amps, Pulse on time 700μs, Pulse off time 0μs..The optimal EDM parameters for multiple performance characteristics for Copper as Electrode are: Peak current 8 Amps, Pulse on time 700μs, Pulse off time 0μs. 3.The most significant EDM parameters in terms of affecting the multiple performance characteristics are Pulse on time and Pulse off time. 4.The performance characteristics in the copper such as material removal rate, electrode wear ratio and surface roughness are together improved by using Modified Taguchi method. Volume: 03 Issue:

7 IJMTES International Journal of Modern Trends in Engineering and Science ISSN: Optimization techniques like fuzzy logic, artificial neural network, grey relational analysis etc can be utilized 6.SEM analysis may be used to check the structural analysis. 7.L7 may be used to improve the efficiency of the results. 8.More number of input parameters may be taken as L7. REFERENCES [] Spedding, T.A., Wang, z.g.,997, parametric optimization and surface characteristic of wire electrical discharge machining process precision of engineering 0,5-5 [] R.Ramakrishnan,L.karunamoorthy, modeling and multi-response optimization of inconel 78 on machining of CNC WEDM process, Journal of material processing technology,008. [3] Scott,D, bovina,s.,rajurkar,t.k.p,99, analysis and optimization of parameter combination in wire electrical discharge machining. International journal of production research 9, [4] S.Singh, Optimization of machining characteristics in electrical discharge machining of 606Al/Al O 3p /0P composites by grey relational analysis, 0. [5] Yih-fong Tzeng and Fu-chen chen, Multi objective optimisation of high speed electrical discharge machining process using a Taguchi fuzzy based approach, 007. [6] S.H.Tomadi, M.A.Hassan, Z.Hamedon, R.Daud & A.G.Khalid, Analysis of the influence of EDM parameters on surface quality, material removal rate and electrode wear of Tungsten carbide, 009. Volume: 03 Issue: