MULTI PARAMETERS OPTIMIZATION OF EDM USING GREY ENTROPY METHOD

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 5, May 2017, pp , Article ID: IJMET_08_05_048 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed MULTI PARAMETERS OPTIMIZATION OF EDM USING GREY ENTROPY METHOD Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina Department of Mechanical Engineering; KL University, Andhra Pradesh, India ABSTRACT PH 17-4 stainless steel material is a martensitic precipitate hardening that yields a tremendous incorporation of high strength. It is hard to machine on traditional machining process. So, Progressive machining process is used. The present work, states the multi reaction advancement approach to govern the machining characteristics on Electrical Discharge Machine (EDM) process during machining operation of PH 17-4 Stainless Steel.PH 17-4 stainless steel is widely used in space engineering, synthetic industries, and in general metal working process. Experiments are conducted using four process specifications such as discharge current, pulse on time, pulse off time, and voltage. Performance specifications like Material Removal Rate (MRR), Surface Roughness (SR), and tool wear rate (TWR) were examined. Initially, Taguchi L 9 orthogonal array (OA) was used to optimize the single process specifications. Finally GRA entropy method was used for optimizing the multi process characteristics. The compelling effect of process specifications are conveyed by ANOVA. Confirmatory tests were done to check the outcomes. Key words: EDM, MRR, PH 17-4 stainless steel, SR, TWR Cite this Article: Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina, Multi Parameters Optimization of EDM Using Grey Entropy Method. International Journal of Mechanical Engineering and Technology, 8(5), 2017, pp INTRODUCTION The material used is PH17-4 (PH) precipitate hardening was also called as age hardening is one of the heat treatment process which increases yield strength. The material has high strength and good corrosion resistance and ability to bear mechanical strength at high temperature. The advantage of using this material was less scaling, high strength to weight ratio. Various applications of PH17-4 stainless steel are aerospace, automotive, and pressure vessels. The ability to bear mechanical strength at high temperature PH17-4 stainless Steel causes difficulty in machining with regular machining process. Due to these difficulties it is hard to machine PH17-4 stainless steel by conventional machining process. So, unconventional machining process is used. EDM is used in machining of composites, super editor@iaeme.com

2 Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina alloys, and ceramics.edm is used in manufacturing automobile parts, moulds, complex shaped dies. Electric discharge machine is used in making dies, which are of complex structures in manufacturing industry. Observations were carried out on the work piece. To reduce the number of sequences we use L 9 Orthogonal Array (OA) method. Taguchi technique was developed by Genichi.Taguchi method was used for single optimization. It is a power full tool for designing and analyzing the problem with various process parameters. It also optimizes the process parameters. Taguchi design of experiment had been used to probe the machining characteristics like material removal rate, surface roughness and tool wear rate by using the variable process parameters like current, pulse on-time, pulse off-time and voltage. But the original Taguchi method is proposed for optimizing the single response parameters. Classic taguchi method was inadequate to solve the multi reaction advancement problem. So, grey theory was scheduled to solve the problem. GRA which was suggested by Deng is a branch of grey system theory. For obtaining the performance of multi machining qualities it was used as a performance index. GRA is applied to study the effect of process specifications on surface integrity of machined surface. In present work grey entropy measurement method was used for calculating weights of several machining characteristics. Confirmatory tests were done to check the outcomes. At present, In finishing process the advancement of preciseness in industries not only require mass production, tolerance, dimensional accuracy but also demand insist profiles, the arise of new and different materials have been developed in the past decagon. Conventional machining operation aim to reach their curb almost more complicate shaped jobs is required. Optimization of variables is needed to achieve maximum performance with a view to ease this difficulty a simple but decisive method based on Taguchi design is used in the present work for examining the response of certain process specifications like Discharge current, pulse on time, pulse off time, and supply voltage at the same time Taguchi method was declined to show the multi reaction advancement problem to overcome this short coming GRA was used. GRA was carried out and found that pulse on time was the main determining factor for MRR, SR and TWR because of easily operated and fast merging GRA has gained great attention and abide successively applied to a vast range applications such as for MRR, SR, TWR and entropy method. GRA entropy method was used to calculate the weight of the performance specifications. In order to calculate the grade values. The multi optimum solution can be set using grades as per priority order. [1] A study was done on process parameters such as pulse on time, pulse off time, wire tension current, upper flash and lower flush using Aluminum HE30.Experiments are planned and conducted using Taguchi design of experiments. Grey relational analysis is used for optimizing the responses like MRR and SR. The Experiment results shows that the combination of pulse on time, pulse off time, wire tension current, upper flash and lower flush are essential for maximization of MRR and Minimization for SR and Kerf Width. [2] Work was carried on Electric discharge machine, during machining of PH17-4 Stainless Steel. Taguchi method was used to analyze the effect and for optimizing the input parameters namely peak current, pulse on time, and pulse off time which gives optimal process characteristics such as Material Removal Rate, Surface Roughness, Tool Wear Rate and Surface Hardness.ANOVA test is used todetermine the influence of process parameters on the responses like MRR, SR,TWR and surface hardness.[3] The optimization of process specifications were done on EDM using copper as electrode material on titanium alloy.the optimum conditions for Material Removal Rate, Surface Roughness and Tool Wear Rate were observed. Micro cracks which are observed on the machined surfaces results in increase with increase in pulse on time and reduces with pulse off time [4] editor@iaeme.com

3 Multi Parameters Optimization of EDM Using Grey Entropy Method Research on Surfactant added EDM of Ti 6Al4V using GRA method was done. Input parameters considered are discharge current, pulse on time, pulse off time and Surfactant concentration in dielectric on the performance responses MRR and SR. Multi optimization on performance responses using GRA method was carried out and stated that pulse on time, pulse off time and Surfactant concentration contributes altogether to multi response optimization. [5] An approach of tungsten carbide composite material using Taguchi and GRA method was performed.here in this taguchi was used to investigate the process parameters for MRR, SR, AE and ROC. GRA was used for optimizing the process parameters, Entropy was used to calculate the grades for finding the optimum solution [6]. Experimentation is conducted using AISI202 stainless steel. In this the machining characteristics of AISI202 had investigated. Experiments were done using taguchi method on conventional and modified generator using EDM. Performance characteristics such a MRR and Surface Roughness were taken. Process Characteristics Such as voltage, discharge current and duty factor were chosen. The discharge current and duty factor significantly affect the machining characteristics and the reduction in discharge current with uniform distribution produces lower surface roughness.[7] The work was done on the application of bio geography based algorithm with process specifications like peak current, pulse on time, duty factor, gap voltage, and dielectric flushing pressure with process responses like MRR, SR, TWR and surface integrity and observed that bio geography based algorithm performs better than other design. [8] The material used for conducting experimentation was AISI304 and machining was done on wire electric discharge machining. Taguchi grey relational has been used for multi optimization process with L 27 Orthogonal Array to optimize the process parameters. Grey relation analysis was carried out for multi optimization. Results were given for multi optimization solution. [9] Machining of Inconel 625 was done using electro chemical drilling process. Taguchi with grey relational analysis is used for optimization of performance characteristics. It was observed that the feed rate is the predominant attribute for the performance specifications. Confirmatory test were also done for verifying the results obtained by GRA approach and observed that there is improvement in the performance with the present approach. [10] The approach used was taguchi for finding the experimental results. The obtained results are used in GRA for optimizing the multi responses. Weights are calculated using Entropy method for obtaining grades for multi optimization. Confirmatory tests were done and results shows that there is a decrease in percentage of surface roughness and tool wear rate. It was found that scarce work was carried out so far on precipitate hardening stainless steel PH17-4. Present work investigates the effect of process specifications like current, pulse on time, Pulse off time and Voltage and their importance on machine responses such as MRR, SR, TWR after the experimental results. Confirmatory tests has been done to verify the optimum setting of the obtained results. From the confirmation it indicates that the combination of optimum machine specifications improve the execution of machine responses. PH17-4 stainless steel has high Strength, less scaling, and corrosion resistance which makes it successfully in various applications such as aerospace, automotive, chemical plant, and medical fields. However machining this material in conventional machining would be a problem because ability to bear mechanical strength at high temperature. Therefore most preferable unconventional machining process EDM is used to machine PH17-4 material. Hence this paper presents multi parameters optimization of EDM using PH17-4 stainless steel for better machining achievement in terms of MRR, SR and TWR editor@iaeme.com

4 Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina 2. EXPERIMENTAL SETUP The experiments were carried out using EDM machine of model Electrapulse CNC EDM (Die sinking type) with positive polarity and servo head mechanism.dielectric fluid used was deionised water. PH17-4 stainless steel was used as workpiece for conducting the experiments in die sink EDM machine. The material is in the form of rectangular bar with dimensions (28 Χ 5 Χ 76) mm. Before conducting the each experiment top surface of the workpiece and bottom surface of the tool are polished with various emery grade papers. The mass loss was calculated to find the MRR and TWR. The mass of the work piece and electrode are measured before and after experiments using an electronically digital weighing balance (Make: CITIZEN, India) having a maximum capacity of 300grams with a resolution of grams were used. The tool and work piece were connected to the negative (cathode) and positive (anode) terminals to the power controller of EDM machine. After compilation of the each experiment tool and work piece were removed, cleaned, dried and weighed. Machining time of all the experiments was calculated by using EDM timer for 1mm depth of hole Design of Experiments using Taguchi method To obtain the ideal and accurate results DOE technique was used to collect the data. In orthogonal array (L 9 ) four process specifications Current (Amp), Pulse on time (μs), and Pulse off Time (μs), and Voltage (V) were selected in this study and MINITAB software was used to analyze the results. Cut the work piece and electrode to appropriate size and shape. Measure the weight of work piece and tool bit before machining. The work piece and electrode was set up in the EDM machine and servo mechanism is maintained. Selected input parameters were fed to the machine and the values were monitored on the screen. Hole cutting operation was performed on EDM. After the completion of process EDM is stopped automatically repeat the procedure for next sample. Steps involved in Taguchi method identify the design parameters to estimate the number of levels for the variable process specifications. Select the required orthogonal array and assign the design parameters to them. Experiments were done based on orthogonal array layout. Optimum levels were selected for design parameters. Table 1 Process control parameter S.no Parameters Units Levels 1 Current (A) Amp Pulse-on-time(B) Μs Pulse-off-time(C) μs Voltage(D) V Grey Relational Analysis In GRA, the standard esteem is vast the capacity of elements is neglected however if the component measured unit objectives and bearings are distinctive the GRA might not produce correct results. So, the actual data must be done pre-processing. GRA provides an wellorganized solution to the multiple input parameters of the problem Data Pre-Processing It is a step by step process in which changing the unique arrangement to an equivalent arrangement. This is the initial stage of GRA. During this stage, the experimentation outputs of MRR (mm 3 /min), SR (Ra) and TWR (mm 3 /min) are standardized between the extensity of zero and one. And this process is called grey relational generation. There are three distinct editor@iaeme.com

5 Multi Parameters Optimization of EDM Using Grey Entropy Method sorts of information standardization according to the requirement. That is lower is Better (LB), higher is Better (HB), and the Nominal is Better (NB).The standardization equations are expressed as follows. Lower is better (LB): () = () () () () Higher is better (HB): () = () () () () Nominal is better (NB): () = 1 () () () () Where i = 1, 2,3,...,n ;j= 1,2,3,...p; )is estimation of jth element in the i th sequence, () is desired value of jth quality characteristic. () is the biggest value of ()And min () is the least value of ()n is the total experiments done for all output parameters and p is the number of output parameters. For MRR we have took larger the better and for Surface Roughness and Tool Wear Rate we have took lower the better Calculation of Grades by using Grey Coefficient For the grey relational grade to calculate we use discrete measurement method. The grey coefficient is stated to emphasize the relation between reference sequences to the compared sequence. The grey coefficient can be expressed as follows, =., (). i =1,,n; j =1,.,m, () = () () max = max max () () min = min min () () ξ [0, 1] Weight Calculation of Output Parameters by using Entropy Method The average sum of all the grey relational coefficients is the grey relational grade. However the priority of each output parameters to the system may be different.but here entropy method with grades are used. Entropy is defined as a mapping function f i : [0, 1] [0, 1] should satisfy conditions. 1) f i (0) = 0, 2) f i (z) = f i (1 -z ), 3) f i (z) is monotonic increase in range between (0,0.5). Thus the function w e (z) can be expressed as follows. Step 1: () = ( + (1 ) 1 (7) The maximum value for the above given equation occurs at x = 0.5, Therefore e = (3) (4) (5) (6) editor@iaeme.com

6 Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina Step 2: Therefore entropy function is defined as W= (). The steps for calculation of weight of every out parameter is stated above. Step 3: Total of the grey relational coefficients in all groupings D j = Step 4: Normalized coefficient (). j= 1,.n. (9) S =. (10) (. ) Step 5 = Sum of entropy E = ( () ) Step 6: Weight of output representative = (8). (11) (12). (13) 3. RESULTS AND DISCUSSION The experiments were conducted on EDM of PH 17-4 stainless steel using taguchi L 9 OA response analysis. ANOVA is discussed in this section. On various performance characteristics like MRR, SR and TWR Impact of Process Specification on MRR (mm 3 /min) The graph for MRR is shown in Fig (2). The figure shows that MRR increases with the increase in current from 10 to 20 Amps. Current mainly influences density available in the discharge gap. To improve current spark energy is used which results in high density. This heats the work piece results in increasing material removal rate at increased current conditions. MRR increases with increase in pulse on time. Due to pulse off time no material is removed from the work piece. As there is no current supply to the work piece and the existing material. During pulse off time the MRR increases up to 30μs and beyond that MRR decreases. As the MRR is to be higher. The maximum condition is obtained at current 20 A, pulse on time 50 μs, pulse off time 25 μs, voltage 50 v editor@iaeme.com

7 Multi Parameters Optimization of EDM Using Grey Entropy Method Main Effects Plot for SN ratios Data Means 29 Current Pulse on time Pulse off time Voltage 28 Mean of SN ratios Signal-to-noise: Larger is better Figure 2 Main Impact plot for MRR (mm 3 /min) ANOVA is an analysis tool used for determining the importance of output parameters on the performance measures at 95% confidence. And is computed using Minitab. The ANOVA analysis for Material Removal Rate (mm 3 /min), Surface Roughness (Ra) and Tool Wear Rate (mm 3 /min) are shown in the Table. Table 2 shows ANOVA for MRR. It was found that for MRR, current was considered as a main factor with a commitment of 72.12% and taken after by pulse on time and pulse off time, with a commitment of 23.32%, and 1.78% respectively. Voltage is found to be an insignificant parameter with contribution of 0.00%. Table 2 ANOVA for MRR Process parameters DF Seq SS Adj MS F-ratio Percentage contribution Current (A) Pulse on Time (B) Pulse off time(c) Voltage(D) Total Impact of Process Specification on SR (Ra) It is observed that current has a leading impact on SR.The figure (3) shows that SR takes place with an increase in current. The explanation for this can be that an increase in discharge current causes a Corresponding increase in the spark energy which results in formation of broad and greater pits.which intern results in increased SR. The figure indicates that SR increases with increase in pulse on time.as increase in the spark energy over terminals which will make bigger size material to be wiped from the work surface degrading the nature of work surface. The conditions are maximum at Current 20 A, Pulse on Time 75 μs Pulse off Time 25 μs Voltage 50 V editor@iaeme.com

8 Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina Main Effects Plot for SN ratios Data Means Current Pulse on time Pulse off time Voltage Mean of SN ratios Signal-to-noise: Smaller is better Figure 3 Main Impact plot for SR (Ra) Table 3 shows analysis of variance for SR pulse on time was considered as an important factor with a contribution of 58.30% and taken after by current, pulse off time and voltage with a contribution of 29.55%, 3.55% and 2.26% respectively. Table 3 ANOVA table for SR Process parameters DF Seq SS Adj MS F-ratio Percentage contribution Current (A) Pulse on Time (B) Pulse off time(c) Voltage(D) Total Impact of Process Specifications on TWR (mm 3 /min) The figure (3) shows that eroding of tool takes rapidly with increment in current. Increment in current increases the spark energy and huge measure of warmth is produced across the anodes bringing about high softening and vanishing of terminals. The figure shows that increase in pulse on time surface roughness increases. From the graph it was shown that pulse off time and voltage does not contribute much for variation in tool wear rate. The tool wear rate conditions are maximum at 10 A current and 30 μs pulse on time. The maximum condition is at current 10 A, pulse on time 30 μs, pulse off time 25 μs, voltage 50 v respectively Main Effects Plot for SN ratios Data Means Current Pulse on time Pulse off time Voltage 1 0 Mean of SN ratios Signal-to-noise: Smaller is better Figure 4 Main impact plot for TWR (mm 3 /min) editor@iaeme.com

9 Multi Parameters Optimization of EDM Using Grey Entropy Method Table 4 shows that.pulse on time was considered as an important factor with a contribution of 45.77% and taken after by current with a contribution of 39.79% voltage and pulse off time was considered to be an insignificant parameter with contribution of 0.58% and 0.35% respectively. Table 4 ANOVA table for TWR Process parameter Degrees of freedom Seq SS Adj MS F-ratio Percentage contribution Current (A) Pulse on Time (B) Pulse off time ( C) Voltage(D) Total 3.4. Multi Objective Optimization using GRA Grey relation coefficients can be calculated using equation (3) and grade can be calculated by taking the average of the corresponding performance characteristics. Corresponding grey relation coefficients with ranks are shown in the table 6. Experiment 1 having the high grade value which shows that corresponding set of process has multi performance characteristics among the all trials. The bigger grade value indicates that the corresponding set of process parameters are nearer to obtain better multi performance characteristics machining performance. S.no Curren t (A) Table 5 Experimental design usingl9 orthogonal array Pulse-ontime(μs) Pulse-offtime(μs) Gap voltage (V) MRR (mm 3 /min) SR (Ra) TWR (mm 3 /min) W= () Table 6: Individual grey relational coefficients S.no MRR TWR SR editor@iaeme.com

10 D j = Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina (). j= 1,.n. Table 7 Sum of grey relational coefficients MRR TWR SR Table 8 Calculation of () S.no MRR SR TWR Normalized coefficient S = = (. ) = ( () ). Table 9 Entropy of each quality characteristics S.no MRR TWR SR Table 10 Calculation of Entropy MRR SR TWR Weight of each output characteristic = (1 ) (1 ) Table 11 Weight of each quality characteristic MRR SR TWR editor@iaeme.com

11 Multi Parameters Optimization of EDM Using Grey Entropy Method The calculated entropies ( ) for MRR, SR and TWR were , and respectively. The calculated weights for MRR, SR and TWR are , and respectively Optimum Level Selection for EDM of PH17-4 The response graph for multi performance characteristics of EDM of PH17-4 is shown in the figure (5). It is observed from the analysis that best possible set of process specifications for Current (A), pulse on time (μs), pulse off time (μs) and Voltage (V)are 10, 30, 20, and 30. Table 12 Calculation of grade value S.NO MRR(mm 3 /min) SR(Ra) TWR(mm 3 /min) GRADEVALUE RANK Table 13 Conformation test results S.NO PARAMETERS OPTIMUM CONDITION VAUES 1 MRR 20A-75μs-25 μs-50v SR 10A -30 μs- 20 μs -40V TWR 10A- 30 μs -20 μs -30V MULTI-OPTIMIZATION 10A- 30 μs- 20 μs- 30V The above results clearly shows that MRR, SR and TWR are decreased when compared with the initial readings. MRR is higher when compared to initial readings. When MRR is higher SR and TWR should be lower. MRR sacrifice for the improvement of SR and TWR. 4. CONFORMATION TEST FOR EDM It is an important stage to conform the results based on taguchi based grey analysis. The result obtained by the multi optimization is verified by conducting conformation test to obtain optimum machining specification s such as current, pulse on time, pulse off time and Voltage with 10 A, 30 μs, 20 μs and 30 V 5. CONCLUSION This paper gives the information about the Multi parameters optimization of EDM using grey entropy method with PH 17-4 STEEL as a work piece material. The final judgments were drawn from this experiment We moved from single optimization technique by Taguchi to multi optimization technique by grey entropy for taking optimum results for the performance characteristics. We have took initial weight values to find grey values and entropy. From the above analysis to obtain multi optimization solution the parameters with their corresponding values are to be taken for current, pulse on time, pulse off time and voltage are 10 A 30 μs 20 μs 30 v respectively. The weights obtained for MRR, SR and TWR are , and editor@iaeme.com

12 Murahari Kolli, Sk. Khadar Basha, Nandana Kumar Midatana, Manindra Bedhapudi, Sai Srikar Desina The optimum machining performance of EDM on PH 17-4work piece for Material Removal Rate, Surface Roughness and Tool Wear Rate are (mm 3 /min), 5.18(Ra) and 0.121(mm 3 /min)produces better machining performance. The conformation test shows that MRR sacrifices its condition for the improvement of SR and TWR The proposed taguchi based grey entropy method is best suitable for multi performance optimization REFERENCES [1] Chengal Reddy, V.; Deepthi, N.; Jaya Krishna, N. Multi Response Optimization of wire EDM on Aluminum HE30 by using Grey Relational Analysis. 4 International Conference on Materials processing and characterization. 2(2015), pp doi: /j.matpr [2] Vikram Reddy, V.; Madar Valli, P.; Kumar, A.; Sridhar Reddy, CH. Influence of Process parameters on characterization of EDM of PH 17-4 Stainless Steel. Journal of advanced Manufacturing Systems.14, 3(2015), pp doi: /s [3] Vijay Verma.; Ram Sajeevan. Multi Process parameters Optimization of Die Sinking EDM on Titanium alloy (Ti6Al4V) using Taguchi approach. 4 International Conference on Materials processing and characterization. 2(2015), pp [4] Murahari, Kolli; Kumar, Adepu. Optimization of the parameters for the Surfactant added EDM of a Ti-6Al-4V Alloy using the GRA Taguchi method.//materials and technology 50(2016) 2, pp doi: /mit [5] Kamal Jangra.; Sandeep Groover.; Aman Agarwal.; Optimization of multi machining characteristics of WEDM of WC-53% Co composite using integrated approach of Taguchi, GRA and entropy method. Frontier of Mechanical Engineering. 7(3), 2012, pp Doi /s [6] Muthurmalingam, T.; Mohan, B.; Influence of discharge current pulse on machinability in electrical discharge machining Material manufacturing process. 28 (2013), pp [7] Mukherjee, R.; Chakraborty, S.; Selection of EDM process parameters using biogeography-based optimization algorithm Material Manufacturing Process. 27(2012) [8] Bijo Mathew.; Benkim, B, A.; Babu, J. Multi process parameters of Optimization of WEDM on AISI304 Using Utility Approach. International Conference on Advances in Manufacturing and Materials Engineering. 5(2014) [9] Manikandan, N.; Kumanan, S.; Sathiyanarayanan, c. Multi Performance Optimization of electro chemical drilling of Inconel 625 using Taguchi based Grey Relational Analysis. Engineering Science and Technology an International Journal [10] Amit Sharma.; Vinod Yadava. Optimization of cut quality characteristics during Nd: YAG laser straight cutting of Ni based super alloy thin sheet using Grey Relational Analysis with Entropy measurement. Materials and Manufacturing process 26 (2011), pp DOI: / [11] Manish Saini, Rahul sharma, Abhinav, Gurupreet Singh, Prabhat Mangla and Er.Amit Sethi. Optimizations of Machining Parameter in Wire EDM For 31 6l Stainless Steel by Using Taguchi Method, Anova, and Grey Analysis. International Journal of Mechanical Engineering and Technology, 7(2), 2016, pp editor@iaeme.com