Evaluation of Optimal Parameters for Machining Of SS 410 With EDM Using Grey Relational Analysis

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 11, 2016 | ISSN (online): 2321-0613

Evaluation of Optimal Parameters for Machining of SS 410 with EDM using Grey Relational Analysis Abhimanyu Chauhan1 Deepak Gupta2 1 M.Tech. Student 2Assistant Professor 1,2 Department of Mechanical Engineering 1,2 Glaxy Global Group of Institutes Abstract— As the advancement in the technology some new material are comes in a trend which offers high strength, hardness and resistance to heat. Some of the materials are titanium, tungsten carbide boride etc. These materials are difficult to machine. A Die Sinker Electrical Discharge Machining (EDM) is a non-conventional and popular machining method. It is used for manufacture punches, dies and press tools because of its capability to produce complicated, intricate shapes and used for machining hard materials. Some common factors like Pulse on time, pulse off-time, current and voltage affects the quality of the final product. The objective of the present study is to optimize the value of the input parameters for material removal rate (MRR), tool wear rate (TWR). Taguchi’s method was used for conducting the experiment. Stainless steel 410 possess high strength, hardness and toughness and selected for experimentation. Copper was used as electrode material.

Starting from identification of value of the input parameters, experiments were performed using L9 orthogonal array.

After conducting the experiments, the results were analyzed with the help of MINITAB 16 software. After conducting the experiment it was analyzed that the optimize conditions found for MRR & TWR were, discharge current (15 A), pulse-on time (45µs).

Key words: EDM, Machining of SS 410 I. LITERATURE REVIEW Lin (2004) studied taguchi method with grey relational analysis for optimizing turning operations with multiple performance characteristics. A grey relational grade obtained from the grey relational analysis is used to solve the turning operations with multiple performance characteristics.

Kuo et al.(2007) studied grey-based Taguchi method to solve the multi-response simulation problem. The grey-based Taguchi method is based on the optimizing procedure of the Taguchi method, and adopts grey relational analysis (GRA) to transfer multi-response problems into single-response problems.

Hsiao et al.(2008) investigated the optimal parameters of plasma arc welding (PAW) by the Taguchi method with Grey relational analysis. The Grey relational grade is used to find optimal PAW parameters with multiple response performance characteristics. The welding parameters (welding current, welding speed, plasma gas flow rate, and torch stand-off) are optimized with consideration of the multiple response performance characteristics (the penetration of root, the weld groove width, and the weld pool undercut.

Lin et al. (2009) investigated machining performance of conductive ceramics (Al2O3 + 30vol% TiC) using electrical discharge machining (EDM) is the aim of this work. The EDM machining parameters such as

machining polarity, peak current, auxiliary current with high voltage, pulse duration, no load voltage, and servo reference voltage were chosen to explore the effects on material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR).

Bhaduri et al. (2009) investigated electro discharge machining (EDM) has been carried out to machine the material. Energy dispersive X-ray spectroscopy and X-ray diffraction analysis have also been carried out on the composite matrix to verify the presence of two distinguishable phases of TiN and Al2O3. The present article reports the effects of EDM process parameters on material removal rate, electrode wear rate, radial overcut, and taper angle while machining TiN–Al2O3 composite.

II. STAINLESS STEEL 410(SS410) SS 410 is general-purpose martensitic stainless steels.

Martensitic stainless steels are fabricated using techniques that require final heat treatment. These grades are less resistant to corrosion when compared to that of austenitic grades. The chemical composition of SS 410 is given in table 1 below.

Contituent % Composition C 0.0672 % Cr 14.37 % Mn 1.286 % Si 0.3481 % P 0.02401 % S 0.18623 % Cu 0.0206 % V 0.0288 % Ni 0.2191 % Mo 0.2877 % Table 1: Chemical Composition of SS410 The chemical composition of SS410 shown in table 3.1 but these can change according to use. The mechanical, physical and electrical properties of SS410 as shown in table 2 Tensile Strength (MPa) 1480 Yield Strength (MPa) 1005 Hardness(HB) 187 Density (kg/m3) 7800 Elastic Modulus(GPa) 200 Specific Heat(J/Kg K) 0-1000 C 460 Thermal Conductivity(W/m.K) at 1000 C 24.9 Electrical Resistivity (nΩ.m) 570 Table 2 Mechanical, Physical and Electrical properties SS410 provides the best combination of wear resistance and corrosion resistance and is used in refineries, oil and gas industries, and chemical plants.

Applications SS 410 is as below:  Bolts, screws, bushings and nuts

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Evaluation of Optimal Parameters for Machining of SS 410 with EDM using Grey Relational Analysis (IJSRD/Vol. 3/Issue 11/2016/071)

 

Shafts, pumps and valves Gas turbines

III. DESIGN OF EXPERIMENT AND DATA COLLECTION

A. Conduct the Experiment The experiment was conducted on EDM machine. The workpiece material used was Stainless Steel AISI 410 in the cylindrical form having 43mm diameter and 130 mm length. This work piece was cut in pieces by power hacksaw shown in figure 5.1.The all nine experiments were run by EDM machine as shown in figure 1. Copper tool of diameter 12 mm was used for experiment.

Controllable factor Level 1 Level 2 Level 3 Current(A) 5 10 15 Pulse on time (µs) 15 30 45 Pulse off time (µs) 8 6 4 Table 3 Selected Factors and levels Pulse on Pulse off Run Current(A) time (µs) time (µs) 1 5 15 8 2 5 30 6 3 5 45 4 4 10 15 6 5 10 30 4 6 10 45 8 7 15 15 4 8 15 30 8 9 15 45 6 Table 4: L9 Orthogonal array for experiment Fig. 1: Experiment Tool wear rate in gm/sec Current Run Pulse on time (µs) Pulse off time (µs) TWR mean (A) TWR1 TWR2 1 5 15 8 0.00250 0.00246 0.002482 2 5 30 6 0.00537 0.00522 0.005295 3 5 45 4 0.00632 0.00732 0.006816 4 10 15 6 0.01043 0.00667 0.008550 5 10 30 4 0.00194 0.00529 0.003614 6 10 45 8 0.00444 0.00407 0.004256 7 15 15 4 0.00533 0.00240 0.003866 8 15 30 8 0.00316 0.00324 0.003200 9 15 45 6 0.00667 0.01622 0.01144 Table 5: L9 Orthogonal array of TWR 9 experiments are conducted for L9 experimental calculated mean TWR as shown in the table 6. So the analysis design. The result of each experiments is repeated two times it is based on two responses of TWR. means for every experiment there is two values of TWR and Tool wear rate in gm/sec Current Run Pulse on time (µs) Pulse off time (µs) S/N ratio (A) TWR1 TWR2 1 5 15 8 0.00250 0.00246 52.1036 2 5 30 6 0.00537 0.00522 45.5213 3 5 45 4 0.00632 0.00732 43.3054 4 10 15 6 0.01043 0.00667 41.1540 5 10 30 4 0.00194 0.00529 47.9896 6 10 45 8 0.00444 0.00407 47.4112 7 15 15 4 0.00533 0.00240 47.6694 8 15 30 8 0.00316 0.00324 49.8946 9 15 45 6 0.00667 0.01622 38.1331 Table 6: Signal to Noise ratio of response for TWR 9 experiments are conducted for L9 experimental calculated S/N ratio for TWR as shown in the table 7. So the design. The result of each experiments is repeated two times it analysis is based on two responses of TWR. means for every experiment there is two values of TWR and Material removal rate(g/sec) Run Current(A) Pulse on time (µs) Pulse off time (µs) MRR mean MRR1 MRR2 1 5 15 8 0.02125 0.02587 0.023561 2 5 30 6 0.00358 0.00696 0.005269 3 5 45 4 0.05684 0.05561 0.056225 4 10 15 6 0.02609 0.02000 0.023043 5 10 30 4 0.11032 0.09000 0.100161 6 10 45 8 0.04667 0.05695 0.051807

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Evaluation of Optimal Parameters for Machining of SS 410 with EDM using Grey Relational Analysis (IJSRD/Vol. 3/Issue 11/2016/071)

7 8 9

15 15 15

15 4 0.06400 0.07920 0.071600 30 8 0.07895 0.08108 0.080014 45 6 0.09333 0.09730 0.095315 Table 7: L9 Orthogonal array of material removal rate 9 experiments are conducted for L9 experimental and calculated mean MRR as shown in the table 5.6.So the design. The result of each experiments is repeated two times analysis is based on two responses of MRR. it means for every experiment there is two values of MRR Material removal rate(g/sec) Run Current(A) Pulse on time (µs) Pulse off time (µs) S/N ratio MRR1 MRR2 1 5 15 8 0.02125 0.02587 -32.6816 2 5 30 6 0.00358 0.00696 -46.9284 3 5 45 4 0.05684 0.05561 -25.0028 4 10 15 6 0.02609 0.02000 -32.9769 5 10 30 4 0.11032 0.09000 -20.1203 6 10 45 8 0.04667 0.05695 -25.8406 7 15 15 4 0.06400 0.07920 -23.0488 8 15 30 8 0.07895 0.08108 -21.9389 9 15 45 6 0.09333 0.09730 -20.4223 Table 8: Signal to Noise ratio of response for material removal rate 9 experiments are conducted for L9 experimental design. The result of each experiments is repeated two times IV. RESULT AND ANALYSIS it means for every experiment there is two values of MRR To study the effect of output responses on the tool wear rate and calculated S/N ratio for MRR as shown in the table 8. and material removal rate using GRA, the data analyzed to So the analysis is based on two responses of MRR better condition of the work. It is necessary which parameter gives lower tool wear rate and higher material removal rate.

The normalized S/N ratios for TWR and MRR are as shown below.

Normalized S/N ratios Run Current(A) Pulse on time (µs) Pulse off time (µs) MRR TWR 1 5 15 8 0.5314 0 2 5 30 6 0 0.4711 3 5 45 4 0.8178 0.6297 4 10 15 6 0.5204 0.7837 5 10 30 4 1 0.2944 6 10 45 8 0.7866 0.3359 7 15 15 4 0.8907 0.3173 8 15 30 8 0.9321 0.1581 9 15 45 6 0.9887 1 Table 9: Normalized signal to noise ratios Deviation sequence Run Current(A) Pulse on time (µs) Pulse off time (µs) MRR TWR 1 5 15 8 0.4686 1 2 5 30 6 1 0.5289 3 5 45 4 0.1822 0.3703 4 10 15 6 0.4796 0.2163 5 10 30 4 0 0.7056 6 10 45 8 0.2134 0.6641 7 15 15 4 0.1093 0.6827 8 15 30 8 0.0679 0.8419 9 15 45 6 0.0113 0 Table 10: Deviation sequence Grey Relational Cofficient Run Current(A) Pulse on Time (µs) Pulse Off Time (µs) MRR TWR 1 5 15 8 0.5162 0.3333 2 5 30 6 0.3333 0.4859 3 5 45 4 0.7329 0.5745 4 10 15 6 0.5104 0.6980 5 10 30 4 1 0.4147 6 10 45 8 0.7008 0.4295 7 15 15 4 0.8206 0.4227

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Evaluation of Optimal Parameters for Machining of SS 410 with EDM using Grey Relational Analysis (IJSRD/Vol. 3/Issue 11/2016/071)

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15 15

30 8 0.8804 0.3726 45 6 0.9778 1 Table 11: Grey Relational Coefficient After this grey relation grade is calculating by averaging the grey relational coefficient corresponding to each performance characteristic as shown in table 11.

Pulse Pulse Grey on off Run Current(A) relational Rank time time grade (µs) (µs) 1 5 15 8 0.4247 8 2 5 30 6 0.4096 9 3 5 45 4 0.6537 3 4 10 15 6 0.6042 6 Fig. 4: Grey relational grade with respect of pulse on time 5 10 30 4 0.7073 2 6 10 45 8 0.5651 7 7 15 15 4 0.6216 5 8 15 30 8 0.6265 4 9 15 45 6 0.9889 1 Table 12: grey relational grade Figure 2 shows the grey relational grade for maximum MRR and minimum TWR. Table 6.4 shows the experimental result for the grey relational grade. The higher value of grey relational grade represents the stronger relational degree between the reference sequence and given sequence. The higher value of grey relational grade means Fig. 5: Grey relational grade with respect of pulse off time that the corresponding cutting parameter is closer to optimal.

From table 6.4 shows current of level 3, pulse on time of Level MaxParameter Level 2 Level 3 rank level 3 and pulse off time level 2 will closer to optimal 1 Min solution.

Current 0.4960 0.6255 0.7456* 0.2496 1 Pulse on 0.5501 0.5811 0.7359* 0.1858 2 time Pulse off 0.5387 0.6675* 0.6608 0.1288 3 time Table 13: Main effect of factors on grey relational grade

Fig. 2: Grey relational grade for maximum MRR and minimum TWR The larger the grey relational grade better is the multiple performance characteristics. However, the relative importance among the machining parameters for the multiple performance characteristics still need to be known, so with the help of table 12 and figure 3, figure 4 and figure 5, the optimal parameter was determined as A3 (current= 15 A), B3 (pulse on time = 45µs) and C2 (pulse off time = 6µs).

A. Predicting Optimum Performance at These Levels Using the optimal parameters, the Optimum output Characteristics (Tool wear rate, Material removal rate) are predicted by Predicted mean= [Average of A3+Average of B3+Average of C2] – 2* Mean of response Predicted mean MRR= [0.06230+0.06778+0.04120]2*0.05633 = 0.17128-0.11266 = 0.05862 g/sec.

Predicted mean TWR= [0.0061695+0.0075046+0.0084291]- 2*0.005026 = 0.0221032-0.01100 =0.01110 g/sec.

V. CONCLUSION 1) Taguchi’s robust design method is suitable to analyze the metal cutting problem as described in the present work.

2) Grey relational analysis was applied in this work for multi response characteristics such as MRR and TWR. 3) In EDM, increase in Current will increases the MRR and TWR within specified test range.

Fig. 3: Grey relational grade with respect of current

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Evaluation of Optimal Parameters for Machining of SS 410 with EDM using Grey Relational Analysis (IJSRD/Vol. 3/Issue 11/2016/071)

4) In EDM, increase in T on will increases the MRR and TWR within specified test range.

5) In EDM, increase in T off will first increases the MRR and TWR then decreases within specified test range.

6) The optimal parameters combination was determined as A3B3C2 i.e. current at 15 A, pulse on time at 45µs and pulse of time at 6µs.

7) From this investigation it has been analyzed that current is most significant factor which affects the output responses.

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