MULTI RESPONSE OPTIMISATION OF DIE SINKER EDM FOR ALSIC COMPOSITE

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 7, Issue 3, May June 2016, pp.63 77, Article ID: IJMET_07_03_006 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication MULTI RESPONSE OPTIMISATION OF DIE SINKER EDM FOR ALSIC COMPOSITE Mukesh Regmi Asst. Professor, Nepal College of Information Technology, Kathmandu Anil Pol Asst. Professor, Department of PG Studies, VTU, Belgaum Sachin Kulkarni Asst. Professor, Gogte Institute of Technology, Belgaum ABSTRACT One of the important aspects that should be taken into consideration in the majority of manufacturing processes and, particularly, in processes related to Electrical Discharge Machining (EDM) is the correct selection of manufacturing conditions. Appropriate choice of the machining parameters and electrode material during electric discharge machining is fundamental to its performance and accuracy. This paper presents a fundamental study of EDM by using two different electrode and aims at investigating the effect of EDM parameters on material removal rate (MRR) and tool wear rate (TWR) as an alternative method for machining Aluminium Silicon Carbide (AlSiC ) metal matrix composite produced with stir casting method. The primary aim of this research is to determine the optimal machining parameter conditions of intensity of current, pulse on time and pulse off time and proper electrode material for machining AlSiC workpiece using EDM. The concept of response surface methodology, with a well-designed experimental scheme named central composite design was used and a second order model capable of predicting the responses is developed. This model was further checked for its adequacy by using ANOVA analysis and the results was further validated by the justification from the related literature. Key words: EDM (Electric Discharge Machining), AlSiC MMC (Aluminium Silicon Carbide Metal Matrix Composite), MRR(Material Removal Rate), TWR(Tool Wear Rate), ANOVA(Analysis of Variance), RSM(Response Surface Methodology) 63 editor@iaeme.com

2 Mukesh Regmi, Anil Pol and Sachin Kulkarni Cite this Article Mukesh Regmi, Anil Pol, Sachin Kulkarni, Multi Response Optimisation of Die Sinker EDM for Alsic Composite. International Journal of Mechanical Engineering and Technology, 7(3), 2016, pp INTRODUCTION Parts manufactured by casting, forming, and various shaping processes often require further operations before they are ready for use or assembly. So a proper machining need to be done which involves the removal of some material from the work piece (machining allowance) in order to produce a specific geometry at a certain degree of accuracy and surface quality in a cheap cost. In modern machining practice, harder, stronger, and tougher materials that are more difficult to cut are frequently used. More attention is, therefore, directed toward machining processes where the mechanical properties of the work piece material are not imposing any limits on the material removal process. In this regard, the nonconventional machining techniques like Electric Discharge Machining (EDM) came into practice [1]. Electric Discharge Machining (EDM), sometimes colloquially also referred to as spark machining, spark eroding, burning, die sinking or wire erosion, is an electrothermal non-traditional machining process, where electrical energy is used to generate electrical spark and material removal mainly occurs due to thermal energy of the spark. Material is removed from the work piece by a series of rapidly recurring current discharges between two electrodes which are separated by a dielectric liquid and subject to an electric voltage. As shown in the Fig. (1) DC power supply provides power to the configuration i.e. tool and the workpiece. The tool is generally given negative polarity and workpiece is given positive polarity. When the voltage across the gap becomes sufficiently high it discharges through the gap in the form of the spark producing very high temperature and thus melting and eroding the material. Figure. 1 Setup of EDM [2] 64 editor@iaeme.com

3 Multi Response Optimisation of Die Sinker EDM for Alsic Composite 2. LITERATURE REVIEW P. Janmanee et al. [3] evaluated the performance of different electrode materials like graphite, copper-graphite and copper-tungsten in EDM of tungsten carbide. The important parameters were discharge current, pulse on time, pulse off time, opencircuit voltage and electrode polarity. Their investigation concluded that MRR increases with the discharge current intensity and graphite electrode gives the most MRR but it gives high electrode wear ratio. B.Mohan et al. [4] had studied EDM of AlSiC composites with vol. % SiC taking the following like Current, electrode material polarity, pulse duration and rotation of electrode on MRR, TWR, and SR. It was observed that the increase in volume percentage of SiC has resulted in decrease in MRR, SR and increase in EWR and the increase in rotational speed of the tube electrode has produced higher MRR, EWR and better SR. Harish K.Garg et al. [5] studied about the machining of the hybrid Aluminium Metal Matrix composite (Al/SiC/Gr and Al/Si10Mg/Fly ash/gr). They investigated about the problems encountered during machining of hybrid MMCs and concluded that machining of Al/SiC-MMC is one of the major problem, which resist its wide spread application in industry and the problems faced were rapid TWR, irregular MRR, requirement of large pulse current values, difficult to cut very complex and complicated shape or geometrical profile etc. S.L.Chen et al. [6] studied about various parameters of EDM like electrode material, pulse duration, discharge current and polarity using two materials namely silicon carbide and tungsten carbide as work piece and copper and copper tungsten as a tool material. They concluded that MRR is directly proportional to current and pulse duration, Electrode wear increased up to 0 µs then started decreasing with increase in pulse duration. They came into conclusion that Copper is better than copper tungsten as an electrode material due to homogeneous wear ratio Objective of the Present Work The objective of the present work is an attempt to finding feasibility of machining Al/SiC composite material using brass as well as copper tungsten electrode. In summary the objective of the project is;- Selection of process variables such as Intensity of current, Pulse on time & Pulse off time in Die-Sink-EDM for machining of Al/SiC Metal Matrix Composite. To investigate the Effect of different tool Electrodes such as brass & CuW on MRR and TWR To develop an empirical model for Intensity of current, Pulse on time & Pulse off time for machining of Al/Sic Metal Matrix Composite using RSM. To verify the lack of fit of the proposed model using analysis of variance (ANOVA). 3. MATERIALS AND METHODOLOGY a) Tool material In this experiment brass and copper tungsten both having 7mm.machining diameter was used as a tool electrode. The important factors in selecting brass and cooper tungsten are their high strength-to-weight ratio, resistance to corrosion by many chemicals, high thermal and electrical conductivity, non-toxicity, reflectivity, appearance and ease of formability and of machinability; they are also nonmagnetic editor@iaeme.com

4 Mukesh Regmi, Anil Pol and Sachin Kulkarni b) Workpiece material The workpiece material chosen was aluminium silicon carbide (AlSiC) metal matrix composite produced with stir casting method consisting of aluminum matrix with 3% silicon carbide particles. It has high thermal conductivity ( W/m K), and its thermal expansion can be adjusted to match other materials, e.g. silicon and gallium arsenide chips and various ceramics. It is chiefly used in microelectronics as substrate for power semiconductor devices and high density multi-chip modules, where it aids with removal of waste heat. c) Response Surface Methodology Response surface methodology (RSM) is a collection of mathematical and statistical techniques that are useful for modeling and analysis of problems in which output or response is influenced by several variables and the goal of RSM is to find the correlation between the response and the variables. It is used in the development of an adequate functional relationship between a response of interest, y, and a number of associated control (or input) variables denoted by x1, x2, xk. Suppose X 1 and X 2 are the factors or parameters of interest of the process and Y i is the maximum yield of the process then the yield is a function of levels of X 1 and X 2 i.e. Y i = f (X 1, X 2 ) + e i (1) where e i represents the noise or error observed in the response Y i. If we denote the expected response by E (Y i ) = f (X 1, X 2 ) = η (2) Then the surface represented by η = f (X 1, X 2 ) (3) is called response surface. If there is curvature in the system, then a polynomial of higher degree must be used, such as the second-order model given in equation 4 (4) Central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needing to use a complete three-level factorial experiment.[7] The CCD used in our experiment is shown below- Table 1 Design Insight Design Central Composite Design Factors 3 Replicates 1 Total runs 20 Base blocks 1 Base runs 20 Total blocks Cube points Center points in cube 6 Axial Points editor@iaeme.com

5 Multi Response Optimisation of Die Sinker EDM for Alsic Composite d) Design Matrix In this study, three machining parameters were selected as control factors and each parameter was designed to have three levels denoted by 1, 2 and 3 respectively. The experiments are planned using CCD in the design of experiments (DOE), which helps in reducing the number of experiments. The total 20 experimental runs were conducted with each tool on the workpiece and results of MRR and TWR was calculated. The process variables with their units and notations are shown in the Table 1 below- Table 2 Parameters and Levels MACHINING RANGE AND LEVELS PARAMETER UNIT Pulse on time µs Pulse off time µs 2 5 Current A 4 6 e) Macine used The experimentation was conducted using EDM, model SAVITA 4631l (Die sinking type) having following specifications- Table 3 Machine Specifications S.N. TYPES Unit Work tank dimensions mm 3 600*370*250 2 Table size mm 2 350*200 3 X-Axis Travel mm Y-Axis Travel mm Z-Axis Travel mm Maximum Job Height mm Maximum Electrode Weight kg 50 Maximum Job Weight kg Dielectric Tank Capacity Lt Machine Weight kg Gross weight kg Day Light mm Throat mm Overall Dimensions m 3 0.*0.* editor@iaeme.com

6 Mukesh Regmi, Anil Pol and Sachin Kulkarni 4. EXPERIMENTATION a) Experimental Procedure Experiment was conducted with negative polarity of electrode. The electrode brass was taken. The diameter of electrode is measured with a micrometer. It was made sure its dimension is according to specification. An initial mass is measured with precision balance. The electrode mass value and the work piece mass value were jotted. The work material (Aluminium Silicon Carbide) was mounted on the T-slot table and positioned at the desired place and clamped. The electrode was clamped on the tool holder, and its alignment was checked. The parameters of the experiment were set regarding Table (3.1) and Table (3.2). The time was set as 2 minutes for the machining of all work materials. Finally, switches ON for operating the desire discharge current values. After machining operation, the electrode was taken out and weighed again on weighing balance. Also the mass value of work piece was taken after machining. The same experiment was repeated with copper tungsten electrode. This experiment is done 20 times for each electrode. The data was fed to the MINITAB where calculation and analysis of results is done. b) MRR and TWR Evaluation The material MRR has been calculated by taking the difference between the weight of the work piece before and after machining to the machining time. Where, MRR= (1) Wb = Weight of work piece before machining in gm Wa = Weight of work piece after machining in gm t = Machining time in minutes TWR is calculated in the same fashion by taking the difference of weight of the tool before and after machining to the machining time. Where, TWR= (1.2) Wbt = Weight of the tool before machining in gm. Wat = Weight of the tool after machining in gm. t = Machining time in minutes 6 editor@iaeme.com

7 Multi Response Optimisation of Die Sinker EDM for Alsic Composite 0.3 MRR with CuW Versus Brass MRR with CuW MRR with Brass Figure. 2 MRR with CuW Versus Brass TWR with CuW Versus Brass TWR with CuW TWR with Brass Figure 3 TWR with CuW Versus Brass The MRR and TWR values were calculated for both the copper tungsten and brass tool experimental run and then plotted on the graph with their corresponding values. 5. RESULTS AND DISCUSSION For interpreting the significant effect of the parameters, a statistical software program called MINITAB version 16 has been used. The experimental results from the tables were analyzed using ANOVA, which is used for identifying the factors significantly affecting the performance measures. The analysis was carried out for the significance level of α=0.1 i.e. for a confidence level of 90%. The sources with the p value less than 0.1are considered to have a statistically significant editor@iaeme.com

8 Mukesh Regmi, Anil Pol and Sachin Kulkarni Results for MRR with CuW Versus Brass Figure. (2) shows the MRR for both the electrodes where it has been conspicuous that CuW gives more MRR than brass for the same machining conditions. Results for TWR with CuW Versus Brass Figure (3) shows the TWR for both the electrodes where it has been clearly seen that Brass has far higher tool wear than that of CuW. Results of MRR with CuW Table 4 Estimated Regression Coefficients for MRR Terms Coeff. SE Coeff. T test P value C * I T on * T off * I* I T on *T on * T off *T off I*T on I*T off T on *T off * S = , R-Sq = 91.55%, R-Sq(pred) = 19.63%, R-Sq(adj) = 3.95% From the ANOVA table, the main effects of pulse on time and pulse off time can be deduced as having significant effect. Thus, the final model correlating Material Removal Rate with cutting parameters is found as follows: The effectiveness of the model is checked by using the R 2 value i.e which is very close to 1 and hence the model is found to be very effective. The validity of the model is reconsidered with the adjusted correlation coefficient i.e. R 2 (adj.) value = 0.3, which is a measure of the variability of the observed output and can be explained by the factors along their factor interactions editor@iaeme.com

9 Mean Multi Response Optimisation of Die Sinker EDM for Alsic Composite Main Effects Plot for MRR Data Means Current Pulse on time Pulse off time Figure. 4 Main Effect for MRR with CuW tool The graph shows that increase in the value of current leads to the significant increase in MRR. This increase of MRR with current is due to the fact that with the increase in amount of pulse current generates strong spark which creates higher temperature, due to which more material is melted and eroded from the workpiece []. The graph reveals that on increasing the pulse on time, MRR goes on increasing up to half the way and it goes on decreasing from 40μs to 60μs. This event has been attributed to the increase of input energy in high pulse on time duration, which results in more chopping on the gap between workpiece and tool electrode, creating a short circuit which decreases the efficiency of electrical spark erosion. In other words short pulse on time duration causes less vaporization, whereas long pulse on time duration causes the plasma channel to expand, resulting in less energy density on workpiece, which is insufficient to melt and/or vaporize the workpiece material [9]. It is also evident that on increasing the pulse off time, MRR goes on increasing from 2μs to 5μs. It is because of correct flushing of the debris with sufficient pulse off time duration; otherwise the debris could make the spark contaminated and unstable, thus decreasing MRR [9]. However it goes on decreasing from 5μs to μs. This is because when pulse off increases, there will be an undesirable heat loss which does not contribute to MRR. This will lead to drop in the temperature of the workpiece before the next spark starts and therefore MRR decreases [] editor@iaeme.com

10 Mean Mukesh Regmi, Anil Pol and Sachin Kulkarni a) Results of TWR with CuW Table 5 Estimated Regression Coefficients for TWR Terms Coeff. SE Coeff. T test P value C * I * T on T off * I* I * T on *T on * T off *T off * I*T on * I*T off T on *T off S = , R-Sq = 90.6%, R-Sq(pred) = 36.49%, R-Sq(adj) = 2.29% Table shows that effect of current and pulse off time terms are found to be statistically significant while except current*pulse on time all interaction terms contributed less significantly to the TWR at 90 % confidence level. Thus, the final model correlating TWR with machining parameters is found as follows: R 2 value of 90.6 % indicates that, the variation in the response can be predicted 90 % correctly by using the above model developed for 90 % confidence interval. Main Effects Plot for TWR Data Means Current Pulse on time Pulse off time Figure. 5 Main Effect for TWR with CuW tool 72 editor@iaeme.com

11 Multi Response Optimisation of Die Sinker EDM for Alsic Composite The graph shows that at the current of A, TWR is found to be the highest than the rest of 2 levels. TWR kept on increasing with the current intensity. The graph reveals that on increasing the pulse on time, TWR goes on decreasing. The reasons for low tool wear rate at longer pulse on time duration settings are mainly due to decreasing spatial current density of discharge channel with increasing discharge pulse on time duration, longer time for heat transfer from the molten crater to the body of tool, which results in less MRR from the crater and higher wear resistance of the tool due to carbon attached to the surface [9]. It is also evident that on increasing the pulse off time, TWR goes on increasing from 2μs to 5μs. However it goes on decreasing from 5μs to μs. b) Results of MRR with Brass Table 6 Estimated Regression Coefficients for MRR Terms Coeff. SE Coeff. T test P value C * I T on T off * I* I T on *T on T off *T off I*T on I*T off * T on *T off S= , R-Sq=95.57%, R-Sq(pred)=65.23%, R-Sq(adj)=91.5% From ANOVA Table below, the main effects of pulse off time can be deduced as having significant effect. Thus, the final model correlating MRR with cutting parameters is found as follows: From Table, it is evident that the model is adequate at 90% confidence level. The effectiveness of the model is checked by using the R 2 value i.e which is very close to 1 and hence the model is found to be very effective. The validity of the model is reconsidered with the adjusted correlation coefficient i.e. R 2 (adj.) value = 0.91, which is a measure of the variability of the observed output and can be explained by the factors along their factor interactions editor@iaeme.com

12 Mean Mukesh Regmi, Anil Pol and Sachin Kulkarni Main Effects Plot for MRR Data Means Current Pulse on time Pulse off time Figure. 6 Main Effect plot for MRR with Brass electrode The graph shows that MRR increase proportionately with the current. The graph reveals that on increasing the pulse on time, MRR goes on increasing up to half the way and it goes on decreasing. This event has been attributed to the increase of the discharge energy of the plasma channel leading to the formation of a bigger molten material crater on the workpiece resulting higher MRR. But consequently by the dispersion of more heat from the spark stricken position and increasing the amount of heat transferred, the plasma channel s efficiency in removing molten material from the crater at the end decreases [10]. It is also evident that on increasing the pulse off time, MRR goes on increasing from. It is because of correct flushing of the debris with sufficient pulse off time duration; which would otherwise make the spark contaminated and unstable, thus decreasing MRR [9]. c) Results of TWR with Brass Table 7 Estimated Regression Coefficients for TWR Terms Coeff. SE Coeff. T test P value C * I * T on T off * I* I * T on *T on T off *T off * I*T on I*T off * T on *T off S = , R-Sq = 97.25%, R-Sq(pred) = 79.50%, R-Sq(adj) = 94.77% 74 editor@iaeme.com

13 Mean Multi Response Optimisation of Die Sinker EDM for Alsic Composite From the ANOVA table above, effect of current and pulse off time terms are found to be statistically significant. Thus, the final model correlating TWR with machining parameters is found as follows: (6) R 2 value of % indicates that, the variation in the response can be predicted 97 % correctly by using the above model developed for 90 % confidence interval. Main Effects Plot for TWR Data Means 0.06 Current Pulse on time Pulse off time Figure. 7 Main Effect plot for TWR with Brass tool The graph shows that at the current of 4A, TWR is found to be minimum. Graph depicted the linear increase in the value of TWR with current. The reason is that, at low current a small quantity of heat is generated and a substantial portion of it is absorbed by the surroundings, as a result, the amount of utilized energy in melting and vaporizing the electrodes is not so intense. But by the increase in pulse current a substantial quantity of heat will be transferred into the electrodes. Furthermore as the pulse current increases, the discharge strikes the surface of the electrode more intensely and creates an impact force on the molten material in the crater and causes more molten material to be ejected out of the electrode [10]. The graph reveals that on increasing the pulse on time, TWR increases half the way and goes on decreasing. Moreover longer pulse on time can provide enough time for heavier positive ions attacking the cathode workpiece and hence removing more material from the work than the tool [11]. It is also evident that on increasing the pulse off time, TWR goes on increasing from 2μs to 5μs and almost showed a constant TWR after that. This is due to the fact that the long pulse duration provides a better heat removal around the surface of brass 75 editor@iaeme.com

14 Mukesh Regmi, Anil Pol and Sachin Kulkarni electrode which is normally a good thermal conductor. The decrease in temperature on the surface of electrode causes less wear on the electrode [4]. Thus the individual effect of pulse on time and pulse off time along with the interaction effect between pulse on and pulse off time have the significant contributions in MRR empirical response models for copper tungsten tool. Whereas the individual effect of pulse off time along with the interaction effect between current and pulse off time have the significant contributions in MRR empirical response models for brass tool. In TWR empirical response models, the individual effect of current and pulse off time have the significant contributions in MRR empirical response models for copper tungsten tool as well as brass tool but interaction effect between current and pulse on time have the significant contributions in TWR with copper tungsten electrode and interaction effect between current and pulse off time have the significant contributions in TWR for brass tool. 6. CONCLUSION Summarizing the main features the following conclusions can be drawn- 1. The predicted machining performance values match the experimental values reasonably well; with R 2 of 91.55% and 90.6% respectively for MRR and TWR using copper tungsten as tool electrode and 95.57% and 97.25% respectively for the MRR and TWR using brass as the tool electrode. 2. It has been observed that MRR as well as TWR goes on increasing with the current. 3. It was observed that MRR goes on increasing with pulse on time till halfway and goes on decreasing for both electrodes while TWR follows the same trend for brass tool but TWR goes on decreasing with pulse on time for copper tungsten. 4. It has also been seen that MRR as well as TWR goes on increasing half the way and decreases with pulse off time using copper tungsten as an electrode but for the brass electrode MRR kept on increasing all the way with pulse off time and TWR kept on increasing half the way and almost remained constant after that. 5. It has been observed that TWR goes on increasing with the current and MRR goes on increasing with pulse on time till halfway and goes on decreasing. 6. Copper Tungsten electrode gave the higher MRR than the brass electrode. Not only that TWR was also very less in CuW as compared to the brass electrode with the same values of the machining conditions. So it is concluded that copper tungsten is better than brass electrode. REFERENCES [1] Hassan Abdel- Gawad-El-Hofy, Advanced Machining Processes, Mc-Graw Hill, Mechanical Engineering Series [2] Dr. A.K. Sharma, Department of Mechanical Engineering, IIT Roorkee [3] P. Janmanee, A. Muttamara, Performance of Difference Electrode Materials in Electrical Discharge Machining of Tungsten Carbide Energy Research Journal 1 (2): 7-90, 2010, ISSN Science Publications [4] B. Mohan, A. Rajadurai, K.G. Satyanarayana, Electric discharge machining of Al SiC metal matrix composites using rotary tube electrode, Journal of Materials Processing Technology (2004) editor@iaeme.com

15 Multi Response Optimisation of Die Sinker EDM for Alsic Composite [5] Harish K.Garg, Ketan Verma, Alakesh Manna, Rajesh Kumar, Hybrid Metal Matrix Composites and further improvement in their machinability, International Journal of Latest Research in Science and Technology ISSN(Online): Vol.1,Issue1:36-44,May-June(2012) [6] S.L.Chen, M.H.Lin, S.F.Hsieh and S.Y.Chiou.; The characteristics of cutting pipe mechanism with multi-electrode in EDM, Journal of material processing technology Vol, 203, (200), pp [7] R.H. Myers, D. C. Montogomery, Response Surface Methodology: Process and Product Optimization using Designed Experiments, 3 rd Edition, John Wiley and Sons [] Mohan Kumar Pradhan and Chandan Kumar Biswas, Modelling of machining parameters for MRR in EDM using response surface methodology, Proceedings of NCMSTA 0 Conference,National Conference on Mechanism Science and Technology: from Theory to Application, November 13-14, 200, National Institute of Technology, Hamirpur [9] M. S. Sohani. V. N. Gaitonde. B. Siddeswarappa. A. S. Deshpande, Investigations into the effect of tool shapes with size factor consideration in sink electrical discharge machining (EDM) process, International Journal of Advanced Manufacturing Technology (2009) 45:1131_1145 [10] M.R. Shabgard, M. Seyedzavvar, S. Nadimi Bavil Oliaei, Influence of input parameters on characteristics of EDM process, Journal of Mechanical Engineering Volume(Year)No, StartPage-EndPage Paper received: x UDC xxx.yyy.z Paper accepted: x [11] S. Assarzadeh, M. Ghoreishi, Statistical modeling and optimization of process parameters in electro-discharge machining of cobalt-bonded tungsten carbide composite (WC/6%Co), The Seventeenth CIRP Conference on Electro Physical and Chemical Machining (ISEM), Procedia CIRP 6 ( 2013 ) editor@iaeme.com