USING TOPSIS METHOD TO OPTIMIZE THE PROCESS PARAMETERS OF D2 STEEL ON ELECTRO-DISCHARGE MACHINING

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 13, December 2018, pp , Article ID: IJMET_09_13_113 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed USING TOPSIS METHOD TO OPTIMIZE THE PROCESS PARAMETERS OF D2 STEEL ON ELECTRO-DISCHARGE MACHINING Sunita Singh Naik* Assistant Professor, Dept. of Mechanical Engg.,VSSUT, Burla Dr. Jaydev Rana Professor, Dept. of Mechanical Engg.,VSSUT, Burla Dr. Prasanta Nanda Professor & Head, Training & Placement Cell,VSSUT,Burla *Corresponding ABSTRACT There are various hard materials are used in the industry such as nickel-chromium super alloy, nimonics, composites, ceramics, carbides and tool steels. So machining on these materials in a very small dimension is a very tough work. So that Electrodischarge machining process is used. In this process both the electrodes are electrically conductive materials. This paper describes machining on D2 Steel with copper electrode. So that the experiment was designed in a orthogonal array and conduct the experiments in this way. Here input current, pulse on time, pulse off time, flushing pressure are the input process parameters and Material removal rate, Tool wear rate, and Surface roughness, percentage of radial overcut, Crack width, Surface crack density are the opuput parameters. Optimize the process parameters of D2 steel on EDM process using TOPSIS method. Then ANOVA test can be carried out and get the most effective parameter. Finally the experimental results were validated by confirmation tests. Lastly compare the experimental results and theoretical results. Key words: TOPSIS, orthogonal array, ANOVA Cite this Article: Sunita Singh Naik, Dr. Jaydev Rana, Dr. Prasanta Nanda, Using TOPSIS Method to Optimize the Process Parameters of D2 Steel on Electro-Discharge Machining, International Journal of Mechanical Engineering and Technology 9(13), 2018, pp editor@iaeme.com

2 Sunita Singh Naik, Dr. Jaydev Rana, Dr. Prasanta Nanda 1. INTRODUCTION The very small part, intricate shape, making of small holes of the hard materials like nickelchromium super alloy, tool steels, titanium,nimonics, temperature resistant alloys, fiberreinforced composites, stellite, ceramics is easily machined in the non-conventional process. These materials are used in the various industries like automobiles, aerospace. In this process the tool and workpiece are not directly connected to each other. There is having some distance. Here the 3 types of energy are used to remove the materials from the workpiece. Electrodischarge machining is one of the advanced machining process in which the gap about 0.5-1mm is maintained between the tool and work piece by a servo system. Both tool and work piece are electrically conducting materials and that are submerged in a dielectric fluid like EDM 30 oil. This is the set up of EDM machine as shown in fig-1. Figure 1 Set up of Electric Discharge Machining In this process the tool and workpiece are connected in a DC power supply and the electrodes are submerged in the dielectric fluid, the spark is produced and that spark is a thermal energy which is used to remove the materials from the workpiece. Sidhant Gupta and etc. [1] state that the pulse on time is more prompting factor than other input parameters when machining on aluminium tool with the AISI D2 steel for evaluating the surface roughness. For MRR, input current is more prompting factor and TWR, pulse off time is more prompting factor. Sanjay kumar Majhi and etc.[2] told that, pulse current and pulse on time increases the specific energy and MRR. Pulse current affects the surface roughness to increase. Pulse off current causes the reduction of tool wear and surface roughness. J.Jeykrishan and etc [3] stated that the significant process parameter is input current followed by pulse off time, pulse on time on the response MRR. Praveen kumar singh and etc [4] told that using the copper and brass electrode on D2 steel, the copper gives better MRR than brass by increasing the current and when we change the gap voltage for both copper and brass, MRR will decrease. Raman K and etc [5] concluded that the relation of the machining parameters and the machinability factor when machining tool steel using EDM. It was concluded that the best performance was given by electrode with the diameter of 20mm at a current setting 6.5 amp having highest MRR and lowest TWR.Shubham Gupta and etc. [6] reported the influence of machining parameters i.e, Discharge current, pulse on time, duty cycle and voltage has been analysed on AISI D2 tool steel as a workpiece. 2. EXPERIMENTAL WORK editor@iaeme.com

3 Using TOPSIS Method to Optimize the Process Parameters of D2 Steel on Electro-Discharge Machining Here the machining on D2 tool steel with copper electrode having 15mm diameter on Electrodischarge machining process. Experiments were designed as a orthogonal array and in this way it was conducted. The entire work can be carried out Electric Discharge Machine model ECOWIN MIC-432CS CNC EDM. Input current, Pulse on time, Pulse off time, flushing Pressure are the process parameters and the MRR-Material removal rate, TWR-tool wear rate, SR-surface roughness, % ROC- Percentage of Radial overcut CW-crackwidth, SCD-Surface crack density are the output parameters. Table-1 shows the input parameters and their levels and Table-2 shows the orthogonal array design and the output parameters. Table 1 Input Parameters and Their Levels Machine Parameters INPUT CURRENT(A) PULSE ON TIME (B) PULSE OFF TIME (C) FLUSHING PRESSURE (D) Symbols Unit LEVELS Ampere µsec µsec k ) Sl no Input Curren t Amp) Table-2: (µs) (µ s) orthogonal array design and the output parameters: FP( ) MRR TWR SR µm %ROC C.W µm SCD µm TOPSIS OPTIMIZATION METHOD Topsis is a optimization method in which the process parameters can be optimized and get the best result among other experiments. This method Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was stated by Hwang Yoon in It was based on the principal of positive ideal solution and negative ideal solution. The Positive ideal solution and negative ideal solution is a hypothetical solution for which all the experimental results are present between maximum and minimum limit. It gives a solution that is not only closest to the hypothetically maximum (best) result but also farthest from the hypothetically minimum (worst) result. The basic thought is to find a solution according to the closenesscoefficient between the feasible solution and the ideal solution. The steps of this method are as follows: Step 1: Development of Normalization the matrix by using the following equation editor@iaeme.com

4 Sunita Singh Naik, Dr. Jaydev Rana, Dr. Prasanta Nanda Where i=1,2,3 mand j=1,2,3.n (1) Xijrepresents i th value of the j th experimental run, ij= corresponding normalized value. Table-3 shows the normalized decision matrix value. Step 2: Assignment of weight of each response variable such as MRR, TWR, SR and %ROC, CW, SCD. Here the weightage value of MRR=0.46, TWR=0.25, SR=0.15, %ROC=0.08, CW=0.04, SCD=0.02 Step 3: Development of weighted normalized decision matrix is obtained by multiplying the normalized decision matrix with its related weights. Vij= ij (2) Where i = 1,2,3.m and j = 1,2,3 n, W ij = weight of the j th attribute. V =weighted normalized value. Here Table-4 shows the weighted normalized decision matrix value. Step 4: The positive ideal solution (V + ) which is for the best possible value and the negative ideal solution (V ) worst value of every attribute is calculated from the weighted decision matrix. For MRR, the positive ideal solution is the higher value whereas for TWR, SR and %ROC, CW,SCD are the lower value. Similarly, for negative ideal solution MRR considers the lower value and for TWR, SR and %ROC, CW, SCD are the higher value. V =minimum/maximum (V V V V ) V = maximum/minimum (V V V V ) Step 5: The Separation distance of every solution i.e the positive ideal solution (S + )and the negative ideal solution (S + ) are calculated by following equation. Table-5 shows the positive and negative ideal solution. S = (V V ) (3) S = (V V ) (4) Where, i= 1, 2, 3 m, j= 1, 2, 3.n Step 6: The closeness co-efficient value (CCO) is determined as follows CCO= Where S = negative ideal solution, S + = positive ideal solution Step 7 According to the ascending order of the closeness- coefficient value the CCO result is ranked. The best combination of parameter is given by the higher CCO value among the other. According to the above mentioned the tables are as follows (5) Table 3 Normalised decision matrix: SL NO Tij-MRR Tij-TWR Tij-SR Tij-%ROC Tij-CW Tij-SCD editor@iaeme.com

5 Using TOPSIS Method to Optimize the Process Parameters of D2 Steel on Electro-Discharge Machining Table 4 Weighted normalized decision matrix: SL NO vij-mrr vij-twr vij-sr vij-%roc vij-cw vij-scd Table-5 Positive and Negative ideal solution: SL NO si+ si- CCO S/N-CCO RANK AVG= Here CCO and Their signal to-noise ratio can be calculated.the Graph-1 state that how the experimental number is related with higher the better of CCO. It shows that serial numer 8 has highest value. Then the response table is obtained and it gives the optimum level and that is shown Table-6. Optimum Level is A3B2C1D1. Fig-2 shows the relation between average response of TOPSIS and their levels editor@iaeme.com

6 AVERAGE RESPONSE OF TOPSIS Sunita Singh Naik, Dr. Jaydev Rana, Dr. Prasanta Nanda C C O CCO Experimental number CCO Graph 1 The experimental number with CCO Table 6 Average response table For Topsis LEVEL-1 LEVEL-2 LEVEL-3 MAX-MIN RANK A B C D B A1 A2 A3 -- B1 B2 B3 -- C1 C2 C3 -- D1 D2 D3 LEVEL Figure 2 Average response of TOPSIS and their level Finally, an ANOVA table is prepared to determine the percentage of contribution of various input parameters to the overall performance characteristics editor@iaeme.com

7 Using TOPSIS Method to Optimize the Process Parameters of D2 Steel on Electro-Discharge Machining Table 7 Anova-Analysis Of Variance: Source of Machining variance parameter DOF SS MS FO % A A B B C C ERROR D D ERROR ERROR TOTAL TOTAL CONFIRMATION TEST γ= +( )= and it matches with A3B2C1D Table 8 The value of optimum and initial value. Optimum experiment INITIAL experiment A3B2C1D A3B2C1D MRR TWR SR OVERCUT CW SCD CCO S/N-CCO CONCLUSIONS From the experiment, we get the optimum levela3b2c1d1 and that value is and it matches with A3B2C1D3= The sequence is input current is 30 amp, pulse on time is 500 µs, pulse off time is 100 µs, flushing pressure is 0.15 k. The CCO value is increased in the optimum level and the signal-to-noise ratio is also increased. So this method is optimized the process parameters significantly. Here the ANOVA table shows that input current is more significant than other parameters. REFERENCES [1] Sidhant Gupta, Dr. S.K.Jain & Gurpinder Singh, Experimental study of MRR, TWR, SR on AISI D2 Steel using Aluminium Electrode on EDM, Global journal of researches in Engineering: AMechanical and Mechanics Engineering, Volume 17 Issue, Year [2] Sanjay kumar majhi, T.K.Mishra, M.K.Pradhan and Hargovind soni, Effect of machining parameters of AISI D2 tool steel on EDM, International journal of current Engineering and technology [3] J.Jeykrishnan, B. Vijaya Ramnath, A. Jude Felix, C. Rupan Pernesh and S. Kalaiyarasan,Parameter optimization of Electro-Discharge Machining in AISI D2 Die editor@iaeme.com

8 Sunita Singh Naik, Dr. Jaydev Rana, Dr. Prasanta Nanda Steel using Taguchi Technique, Indian Journal of Science and Technology, Vol 9(43), DOI: /ijst/2016/v9i43/101972, November [4] Praveen kumar singh, Dinesh kumar Rao, Anshika Gupta, Experimental studies for mrr on AISI D2 steel using EDM, international journal on emerging technology. [5] Raman k, Sathiya G.K, saisujith k,mani p, Effect of machining parameters in Electric discharge machining of D2 tool steel, international journal of science and research. [6] Shubham Gupta, Harsh Pandey, Saurabh Sen, Experimental Investigation of Machining Parameters for EDM using D2 tool steel, International Journal of Innovative Science, Engineering & Technology, Vol.3 Issue 6, june