20-36 Al-Khwarizmi Engineering Journal,Vol. 11, No. Studying and Modeling the Effect of Graphite Powder Mixing Electrical Discharge Machining on the Main Process Characteristics Ahmed N. Al-Khazraji *, ** Department *** Department (Received Abstract This paper concerned with study the effect of a graphite micro powder mixed in the kerosene dielectric fluid during powder mixing electric discharge machining (PMEDM) of high carbon high chromium AISI D2 steel. The type of electrode (copper and graphite), the pulse are taken as the process main input parameters. The material removal rate MRR, the tool wear ratio TWR and the work piece surface roughness (SR) are taken as output parameters to planned using response surface methodology (RSM) design procedure. Empirical models are developed for MRR, TWR and SR, using the analysis of variance (ANOVA).The best results for the productivity of the when using the graphite electrodes, the pulse current (22 A), the pulse on duration (120 µs) and using the graphite powder mixing in kerosene dielectric reaches (82.84mm³/min). The result gives an improvement in material removal rate of (274%) with respect to the corresponding value obtained when copper electrodes with kerosene dielectric alone. The best results for the tool wear ratio (TWR) of the process obtained when using the copper electrodes, the pulse current (8 A), the pulse on duration (120 µs) and using the kerosene dielectric alone reaches (0.31 %). The use of graphite electrodes, the kerosene dielectric with 5g/l graphite powder mixing, the pulse current (8 A), the pulse on duration (40 µs) give the best surface roughness (141%) with respect to the corresponding value obtained when using copper electrodes and the kerosene dielectric alone with the same other parameters and machining conditions. Keywords: EDM, RSM, MRR, TWR, SR, AISI D2die steel, graphite powder mixing 1. Intro duction EDM process is useful for the machining of high-value components, such as mould tools and dies as well as aerospace engine components. The process is particularly advantageous when compared to conventional mechanical cutting operations, since strength and toug work piece are not factors in its machinability, and instead the thermal and electrical properties determine the ability for a material to be cut [1,2]. EDM is known to significantly affect the surface of cut materials compared to many other manufacturing processes, such Khwarizmi Engineering Journal,Vol. 11, No. 3, P.P. 20-36 (2015) Studying and Modeling the Effect of Graphite Powder Mixing Electrical Discharge Machining on the Main Process Characteristics Khazraji * Samir A. Amin ** Saad M. Ali *** Department of Mechanical Engineering / University of Technology Department of Mechanical Engineering / University of Karbala *Email: dr_ahmed53 @yahoo.com **Email: alrabiee2002@yahoo.com ***Email: smaengg@yahoo.com (Received 1 February 2015 ; accepted 11 May 2015) concerned with study the effect of a graphite micro powder mixed in the kerosene dielectric fluid during powder mixing electric discharge machining (PMEDM) of high carbon high chromium AISI D2 steel. The type of ulse current and the pulse-on time and mixing powder in kerosene dielectric fluid are taken as the process main input parameters. The material removal rate MRR, the tool wear ratio TWR and the work piece surface roughness (SR) are taken as output parameters to measure the process performance. The experiments are planned using response surface methodology (RSM) design procedure. Empirical models are developed for MRR, TWR and SR, using the analysis of variance (ANOVA).The best results for the productivity of the when using the graphite electrodes, the pulse current (22 A), the pulse on duration (120 µs) and using the graphite powder mixing in kerosene dielectric reaches (82.84mm³/min). The result gives an improvement in material removal e of (274%) with respect to the corresponding value obtained when copper electrodes with kerosene dielectric alone. The best results for the tool wear ratio (TWR) of the process obtained when using the copper electrodes, the pulse on duration (120 µs) and using the kerosene dielectric alone reaches (0.31 %). The use of graphite electrodes, the kerosene dielectric with 5g/l graphite powder mixing, the pulse current (8 A), the pulse on duration (40 µs) give the best surface roughness of a value (2.77 µm).This result yields an improvement in SR by (141%) with respect to the corresponding value obtained when using copper electrodes and the kerosene dielectric alone with the same other parameters and machining conditions. RSM, MRR, TWR, SR, AISI D2die steel, graphite powder mixing. process is useful for the machining of value components, such as mould tools and dies as well as aerospace engine components. The process is particularly advantageous when compared to conventional mechanical cutting operations, since strength and toughness of the work piece are not factors in its machinability, and instead the thermal and electrical properties determine the ability for a material to be cut [1,2]. EDM is known to significantly affect the surface of cut materials compared to many other as milling, grinding or electrochemical reduced potential fatigue life of EDM components [3]. AISI D2 cold work also known as die steels, is one of the most popular high-chromium and high and it is characterized by its high compressive strength and wear resistance, good through hardening properties, high stability in hardening and good resistance to tempering a high alloy steels Fe-Cr the ability to preserve its desirable mechanical properties intact upon cycling over a range of temperatures, which can be an advantage for applications including, piercing and blanking dies, Al-Khwarizmi Engineering Journal (2015) Studying and Modeling the Effect of Graphite Powder Mixing Electrical Discharge Machining on the Main Process Characteristics Saad M. Ali *** of Technology Mechanical Engineering / University of Karbala concerned with study the effect of a graphite micro powder mixed in the kerosene dielectric fluid during powder mixing electric discharge machining (PMEDM) of high carbon high chromium AISI D2 steel. The type of on time and mixing powder in kerosene dielectric fluid are taken as the process main input parameters. The material removal rate MRR, the tool wear ratio TWR and the work measure the process performance. The experiments are planned using response surface methodology (RSM) design procedure. Empirical models are developed for MRR, TWR and SR, using the analysis of variance (ANOVA).The best results for the productivity of the process (MRR) obtained when using the graphite electrodes, the pulse current (22 A), the pulse on duration (120 µs) and using the graphite powder mixing in kerosene dielectric reaches (82.84mm³/min). The result gives an improvement in material removal e of (274%) with respect to the corresponding value obtained when copper electrodes with kerosene dielectric alone. The best results for the tool wear ratio (TWR) of the process obtained when using the copper electrodes, the pulse on duration (120 µs) and using the kerosene dielectric alone reaches (0.31 %). The use of graphite electrodes, the kerosene dielectric with 5g/l graphite powder mixing, the pulse current (8 A), the pulse on of a value (2.77 µm).This result yields an improvement in SR by (141%) with respect to the corresponding value obtained when using copper electrodes and the kerosene dielectric alone grinding or electrochemical machining, and the reduced potential fatigue life of EDM components tool steels of series D, nown as die steels, is one of the most m and high-carbon steels and it is characterized by its high compressive strength and wear resistance, good through- hardening properties, high stability in hardening and good resistance to tempering-back. AISI D2is Cr-C-base. This alloy has the ability to preserve its desirable mechanical properties intact upon cycling over a range of temperatures, which can be an advantage for applications including, piercing and blanking dies, Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 21 punches, shear blades, spinning tools, slitting cutters, as well as variety of higher-end wood working tools [4] EDM process is very demanding but the mechanism of process is complex and far from completely understood. Therefore, it is hard to establish a model that can accurately predict the response (productivity, surface quality etc.) by correlating the process parameter, though several attempts have been made [5]. Since it is a very costly process, optimal setting of the process parameters are up most important to reduce the machining time to enhance the productivity [6]. Improving the MRR and surface quality are still challenging problems that restrict the expanded application of the technology [7]. Among several attempts, RSM was employed by N. S. Khundrakpam et al [8], have been used a Central Composite Design (CCD) for combination of variables and Response Surface Method (RSM) to explore the influence of process parameters, such as peak current, powder concentration and tool diameter on the Material Removal Rate (MRR) on EN-8 steel. Analysis of Variance (ANOVA) was performed to obtain the significant coefficients. Pradhan and Biswas [9], investigated the influence of processing variables on the responses MRR and SR. Ranganathan and Senthivelan [10], used powder mixing for optimization of SR, TWR and MRR. Pradhan and Biswas [11], have established empirical models variables with MRR and SR. J. Lin and C. Lin [12], optimized the machining parameters with responses MRR, SR, and electrode wear ratio using of orthogonal array. Singh et al. [13], optimized MRR, TWR, SR on EDM. Reddy and Rao [14], obtained the optimal levels of process in drilling of aluminum 6061 alloy using design of experiments based grey relational analysis. Saurav and Sankar [15], studied the effect of parametric influence of wire EDM on MRR, SR and width of cut to establish mathematical models and simulation. B. Reddy et al. [16], studied the effect of fine metal powders, such as aluminum and copper are mixed to the dielectric fluid, during Electric Discharge Machining (EDM) of AISI D3 Steel and EN-31 steel. Material removal rate and Surface Roughness are taken as output parameters to measure the process performance. The obtained outcomes of experiments indicated that the addition of metal powders in dielectric fluid increases the material removal rate and improves the surface quality. This paper attempted to study the effect of graphite powder mixed to the dielectric fluid with other input parameters like, peak current and pulse on time, during Electric Discharge Machining (EDM) of AISI D2die steel. Material removal rate, electrodes wear rates and surfaces roughnesses are taken as output parameters to measure the EDM and PMEDM process performance. This paper is also attempted to develop models for SR, MRR and TWR by using the response surface methodology (RSM) technique. Two sets of experiments are designed for performing the experiments in pure kerosene dielectric for the first set, while the second is the addition of abrasives graphite powders mixed with dielectric fluid in order to improve the process productivity, efficiency and the workpiece surface quality. 2. Experimental Work The selected AISI D2 die steel workpiece material, was tested firstly for chemical composition examination. Three samples were tested by using the AMETEXSPECTRO MAX material analyzer. The results with the equivalent values according to ASTM A 681-76 standard specification for alloy and die steels [17] are listed in table (1). Four specimens were prepared for tensile tests on the bases on ASTM-77 steel standards for flatwork piece [18]. The same specimens were tested for Rockwell hardness tests. The tests results are given in table (2). Two types of electrodes materials, copper and graphite were selected. The electrodes were manufactured with a square cross-section of 24 mm and 30 mm lengths, with a quantity of 24 pieces for each type, as shown in figure (1). The main designed EDM parameters are the gap voltage Vp (140 V), the pulse current Ip (8 and 22 A), the pulse on time duration period time Ton (40 and 120 µs), the pulse off time duration period Toff (14 and 40 µs), the graphite powder concentration (0 and 5g/l), the kerosene dielectric adjusted from both sides of the w/p with a flashing pressure = 0.73 bar (10.3 PSI) and the electrode polarity (+). The EDM experiments were done on ACRACNC-EB EDM machine with all the manufactured attachments shown in figure (2). A stainless steel container (of about 30 liters volume and dimensions 400 mm hight, 300mm length, 230 mm width and plate thickness 3 mm) was manufactured. It contains of a special kerosene dielectric pump, an electric motor (300 RPM) connected to a mixture contains a stainless steel impellers, a workpiece clamping fixture, valves and pipe accessories. For the power supply, an Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 22 AC/DC converter for driving the special kerosene pump was attached in an electrical board. This board contains also a pressure gauge (one bar capacity), wiring, switches and piping accessories. The manufacturing of the stainless steel container were completed by using the TIG argon inert gas welding process, as shown in figure (2). The graphite powders substances were tested for chemical compositions by using the X-Ray diffraction apparatus, and then the powder was tested to measure its grains sizes using the laser diffraction particle size analyzer. The average grain size is (44,866 µm) for graphite powder as given in the test certificates. The surface roughness for each work piece and electrode (copper and graphite) were measured before and after EDM and PMEDM machining by using the portable surface roughness tester. All the w/p specimens and electrodes are weighed before and after EDM machining too by using the electronic weighting balance with accuracy of (0.0001g). Fig. 1. The copper and graphite electrodes and workpieces PMEDM processes. Fig. 2. The (CNC) EDM machine with all the manufactured accessories designed for the implementation the PMEDM experiments. Table 1, The chemical compositions for the selected workpiece material and the equivalent given by the standard for AISI D2die steel. SAMPLE C% Si % Mn % P % S % Cr % Mo% Ni % Co % Cu% V % Fe% Tested samples 1.51 0.174 0.264 0.014 0.003 12.71 0.555 0.158 0.0137 0.099 0.306 Bal. Standard AISI D2 [17] 1.40 to 1.60 0.60 max. 0.60 max. 0.03 max. 0.03 max. 11.00 to 13.00 0.70 to 1.20 - 1.00 Max. - 1.10 Max. Bal. Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 23 Table 2, The mechanical properties for the selected materials. Ultimate Tensile stress (N/mm²) Yield strength (N/mm²) Elongation (%) Hardness (HRB) Average 704.25 415.25 18.125 90.25 3. Results and Discussions 3.1 Modeling of Material Removal Rate (MRR) Using Copper and Graphite Electrodes In this paper, to study the performance characteristics of the process , two groups of experiments are designed using the kerosene dielectric alone or with graphite powder mixing, each contains (22) experiments for comparing the results produced by EDM and PMEDM machining. Each group was divided in two subgroups. The first subgroup used the copper electrodes, while the graphite electrodes were used in the second subgroup. A new set of w/p and electrode was using in each experiment. The surface roughness (SR), the material removal rate (MRR) and the tool wear ratio (TWR), which are experimentally measured and calculated after EDM and PMEDM machining with the input parameters are modeled by using the response surface methodology (RSM) and the two level factorial (2³) design for both experimental groups. The input EDM parameters and their levels are given in table (3), while the output process responses are given in table (4). The designed EDM experimental matrix in a random manner with the selected actual factors and the experimental response results for the both groups using the kerosene dielectric or the kerosene dielectric with graphite powder mixing with copper and graphite electrodes are collected in one matrix as given in table (5). The two level factors (2�) full factorial design (FFD) was used to set the necessary number of experiments to fit the model. The ANOVA technique was used to analyze the significance of EDM process parameters, where the F-test ratio is calculated for a 95% level of confidence. The ANOVA functions then run in order to assess the results for the material removal rate (MRR) response which are given in table (6) using the two levels and three factor for backward Partial sum of squares transform model for lower the p-value. The Model F-value of 107.11implies the model is significant. Values of "Prob> F" less than 0.0500 indicate model terms are significant. In this case, A, B, C, D, AB, BC, BD, CD are significant model terms. The predicted final empirical equation is: Material removal rate (MMR) = + 1.88640- 4.63234 * A + 0.66256 * B - 0.12830* C - 12.03147*D + 0.49159*A*B + 0.018299* B*C+0.79800*B*D+0.078758*C*D …(1) The three dimensional (3D) graphs given in figures (3-6) are used to interpret and evaluate the model for the experimental groups. These figures show the influence of the EDM and PMEDM parameters on the material removal rate. All figures indicated that material removal rate is increasing with increasing the pulse current (up to 22 A) and the pulse on duration (up to120 µs). Figure (3) and table (5) indicated that when using these levels of parameters with the copper electrodes, MRR reaches theoretically (28.2177 mm³/min), and experimentally (30.2452 mm³/min). When using the graphite powder mixing in kerosene dielectric, MRR reaches the value (58.1689 mm³/min), as shown in figure (5) and the experimental value is (58.0063 mm³/min). This means that the process removal rate increase by (206 %) when using the graphite powder compared with when using the kerosene dielectric alone. The same results obtained when working with graphite electrodes and kerosene dielectric alone, where the maximum productivity of the process obtained reaches a value (40.5832 mm³/min) as shown in figure (4), whereas the experimental value is (37.4865 mm³/min). The predicted MRR reaches a value of (70.5344 mm³/min) with the same previous parameters and using the graphite powder mixing in kerosene dielectric as shown in figure (6) and the experimental value is (82.8404 mm³/min), i.e., the predicted MRR process improved by (174 %) and experimentally by (221 %). The total predicted improvement of the MRR process is (250 %); experimentally by (274 %) with respect to using the graphite electrode with graphite powder mixing and compared with the case when using the copper electrodes and the kerosene dielectric alone. This means that productivity increases with the pulse current and pulse on duration time, especially when using the graphite electrodes and graphite powder mixing. The amount of thermal energy generated would be great and it is working to increase the melting and abrasive processing to remove successive more layers of workpiece Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 24 surface. This energy will increase with increasing the pulse current period, especially when using the graphite powder mixed in kerosene dielectric, which owns high level of hardness and abrasiveness and working to increase the removal property of the process. The high thermal conductivity of the graphite electrode and the graphite powder also works to increase the amount of thermal energy transformed to the workpiece surface, thereby improving removal and productivity efficiency. The high electrical conductivity of the graphite powder is working to increase the electrical conductivity of the kerosene dielectric and this will improve the discharge characteristics of the process by increase and intensify the arrangement and intensity of discharge energy bands consequently improved the material removal rates. 3.1.1. Numerical Optimization of Material Removal Rate Results For optimization and to development of the predicted model with the best EDM and PMEDM parameters, a set of new goals for the MRR response will be conducted to generate the optimal combination conditions for these parameters. The new objective function named the desirability will allow evaluating the goals by a proper combining. The main goals are to maximize the values of response with the same ranges of the selected EDM parameters and electrodes types as mentioned in table (7). The best three solutions found from the desirability process shows that the optimum predicted values of the MRR obtained when using the graphite electrodes with pulse current about (22 A), pulse of duration about (120 µs) and using the graphite mixed powder gives the best maximum predicted MRR of (70.534mm³/min) with a maximum desirability ratio (0.839) as shown in table (8). The desirability process shows that the best predicting response values are approximately the same with the obtained values by experiments as indicated in table (5), experiments number (28) and (35) with the same input parameters where the experimentally values of MRR obtained are (82.8404) and (74.1234) mm³/min respectively and this confirmation the theoretical results of the present work. Table 3, The input EDM parameters and their levels for both groups. Fac. Name Units Min. Max. Coded Values Levels A Pulse current (Ip) (A) 8 22 -1 +1 2 B Pulse on duration (Ton) (µs) 40 120 -1 +1 2 C Graphite powder mixed in kerosene dielectric g/l 0 5 -1 +1 2 Table 4, The EDM process responses, MRR, TWR and SR. Response Name Units Minimum Maximum Trans Model R1 Material removal rate(MMR) mm³/min 6.1696 82.8404 None R2FI R2 Tool wear ratio(EWR) % 0.4168 12.8845 None R3FI R3 Surface roughness (SR) µm 2.77 6.32 Inverse R3FI Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 25 Table 5, The designed experimental matrix for Group (1) using copper electrodes. Block No. Run No. Input factors(Actual) Responses X1 X2 X3 X4 Material removal rate (MMR) (mm³/min) Tool wear ratio (EWR) (%) Surface roughness (SR) (µm) A: type of electrode B: Pulse current (Ip) (A) C: Pulse on duration (Ton) (µs) D:Graphite powder mixed in kerosene dielectric (g/l) 1 1 Copper 22 120 0 26.7538 1.898 5.65 1 2 Graphite 8 40 5 7.9017 12.8845 2.78 1 3 Graphite 8 120 0 7.2612 3.0141 4.75 1 4 Graphite 22 120 0 35.6832 1.5401 5.31 1 5 Graphite 8 120 5 7.4974 11.3743 5.36 1 6 Graphite 22 40 5 37.1668 4.9357 4.63 1 7 Graphite 8 40 0 8.5929 7.0756 2.87 1 8 Copper 8 120 0 9.3969 0.4168 3.91 1 9 Copper 8 40 5 9.4955 4.396 3.77 1 10 Graphite 22 120 5 74.062 1.7828 6.28 1 11 Copper 22 40 5 29.5841 5.0271 5.24 1 12 Copper 22 40 0 15.9392 6.0467 4.84 1 13 Copper 8 120 5 14.2975 1.582 4.88 1 14 Copper 22 120 5 55.0778 1.6091 6.19 1 15 Copper 8 40 0 6.2369 3.1489 4.05 2 16 Copper 22 40 0 15.8625 5.9988 4.85 2 17 Graphite 22 120 0 37.4865 1.0934 6.26 2 18 Copper 22 120 0 25.8697 1.9535 5.63 2 19 Copper 22 120 5 58.0663 1.7499 6.21 2 20 Graphite 8 40 0 7.1359 7.0756 2.9 2 21 Graphite 22 40 0 29.1021 3.1563 3.78 2 22 Copper 8 120 5 14.0783 1.5006 4.92 2 23 Graphite 8 40 5 8.1076 12.1329 2.77 2 24 Copper 8 40 0 6.8461 2.764 4.07 2 25 Graphite 8 120 5 9.0389 8.9295 5.32 2 26 Graphite 8 120 0 6.8553 2.9656 4.73 2 27 Copper 8 120 0 8.4774 0.5054 3.94 2 28 Graphite 22 120 5 82.8404 1.6076 6.32 2 29 Copper 8 40 5 7.1469 4.5038 3.81 3 30 Graphite 22 40 5 31.4558 5.8591 4.46 3 31 Graphite 22 40 0 29.1021 3.1563 3.78 3 32 Graphite 8 120 0 6.1696 2.9883 4.77 3 33 Copper 8 40 0 7.4271 2.7986 4.09 3 34 Copper 8 120 0 9.2215 0.4273 3.94 3 35 Graphite 22 120 5 74.1234 1.6262 6.3 3 36 Graphite 8 40 0 12.1531 5.7332 2.81 3 37 Copper 8 120 5 9.7263 1.5738 4.9 3 38 Copper 22 40 5 30.343 5.0822 5.25 3 39 Copper 22 120 5 55.9944 1.5594 6.17 3 40 Graphite 8 120 5 10.277 8.9295 5.34 3 41 Graphite 22 120 0 36.3096 1.376 6.24 3 42 Copper 22 120 0 30.2452 1.9765 5.61 3 43 Copper 8 40 5 9.6572 4.05 3.79 Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 26 Table 6, The (ANOVA) analysis for material removal rate (MRR) after the EDM. Source Sum of Squares df Mean Square F Value p-value Prob> F Block 52.40 2 26.20 Model 17331.15 8 2166.39 107.11 < 0.0001 significant A-type of electrode 171.34 1 171.34 8.47 0.0065 B-Pulse current (Ip) 161.21 1 161.21 7.97 0.0081 C-Pulse on duration (Ton) 209.58 1 209.58 10.36 0.0029 D-graphite powder mixed in kerosene dielectric 667.47 1 667.47 33.00 < 0.0001 AB 500.48 1 500.48 24.75 < 0.0001 BC 1089.99 1 1089.99 53.89 < 0.0001 BD 1299.28 1 1299.28 64.24 < 0.0001 CD 412.07 1 412.07 20.37 < 0.0001 Residual 647.20 32 20.23 Cor Total 18030.76 42 Fig. 3. The 3D graphs for MRR using kerosene dielectric alone and copper electrodes Fig. 4. The 3D graphs for MRR using kerosene dielectric alone and the graphite electrodes. D: SiC powder mixed in kerosene dielectric = 0 40 60 80 100 120 8 10 12 14 16 18 20 22 0 20 40 60 80 100 M at er ia l r em ov al r at e( M M R ) (m m ³/ m in ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) 40 60 80 100 120 8 10 12 14 16 18 20 22 0 20 40 60 80 100 M at er ia l r em ov al r at e( M M R ) (m m ³/ m in ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) Design-Expert® Software Factor Coding: Actual Material removal rate(MMR) (mm³/min) Design points above predicted value Design points below predicted value 82.8404 6.1696 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Copper D: SiC powder mixed in kerosene dielectric = 0 Design-Expert® Software Factor Coding: Actual Material removal rate(MMR) (mm³/min) Design points above predicted value Design points below predicted value 82.8404 6.1696 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Graphite D: SiC powder mixed in kerosene dielectric = 0 Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 27 Fig. 5. The 3D graphs for MRR using kerosene dielectric with graphite powder mixing (PMEDM) and the copper electrodes. Fig. 6. The 3D graphs for MRR using kerosene dielectric with graphite powder mixing (PMEDM) and the graphite electrodes. 3.2 Modeling of Tool Wear Ratio Using Copper and Graphite Electrodes The ANOVA technique for the tool wear ratio (TWR) response which are given in table (9) using the three factor backward levels for transform partial sum of squares model for lower the p- value. Table 7, The new constraints goals for optimization the MRR of the process. Name Goal Lower Limit Upper Limit Lower Weight Upper Weight Importance A:type of electrode is in range Copper Graphite 1 1 3 B:Pulse current (Ip) is in range 8 22 1 1 3 C:Pulse on duration (Ton) is in range 40 120 1 1 3 D:Graphite powder mixed in kerosene dielectric is in range 0 5 1 1 3 Material removal rate(MMR) maximize 6.1696 82.8404 1 1 3 D: SiC powder mixed in kerosene dielectric = 5 40 60 80 100 120 8 10 12 14 16 18 20 22 0 20 40 60 80 100 M at er ia l r em ov al r at e( M M R ) (m m ³/ m in ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) D: SiC powder mixed in kerosene dielectric = 5 40 60 80 100 120 8 10 12 14 16 18 20 22 0 20 40 60 80 100 M at er ia l r em ov al r at e( M M R ) (m m ³/ m in ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) Design-Expert® Software Factor Coding: Actual Material removal rate(MMR) (mm³/min) Design points above predicted value Design points below predicted value 82.8404 6.1696 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Copper Design-Expert® Software Factor Coding: Actual Material removal rate(MMR) (mm³/min) Design points above predicted value Design points below predicted value 82.8404 6.1696 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Graphite Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 28 Table 8, The desirability process for optimization of the predicted MRR. No. Type of electrode Pulse current (Ip) (A) Pulse on duration (T on) (µs) SiC powder mixed in kerosene dielectric gm/l Material removal rate (MMR) mm³/min Desirability 1 Graphite 22.000 120.000 5 70.534 0.839 Selected 2 Graphite 22.000 119.162 5 70.239 0.836 3 Graphite 21.860 120.000 5 69.954 0.832 The Model F-value of 139.45 implies the model is significant. Values of "Prob> F" less than 0.0500 indicate model terms are significant. In this case, A, B, C, D, AB, AD, BD, ABC, ABD are significant model terms. The predicted final case, equation is: Tool wear ratio (TWR)= + 9.29683 + 5.42634 * A - 0.13523 * B -0.038354*C + 2.92512*D- 0.28365 * A * B - 9.40295 E-003 *A + 1.77480 * A* D - 0.12766 * B * D + 6.81399 E – 004 * A * B * C- 0.062467 * A * B * D …(2) The three dimensional (3D) graphs given in figures (7 - 10) show the influence the EDM and PMEDM parameters on the tool wear ratio. Figure (7) indicates that when using the pulse current (8 A) and pulse on duration (40 µs), the tool wear ratio decreased, reaching the values (3.05%) when using the copper electrodes and kerosene dielectric alone and (3.68%) when using the graphite electrodes and the kerosene dielectric alone with pulse current (22 A), as shown in figure (8). Figure (9) depicts the 3D graphs for TWR using the pulse current (8 A) and pulse on duration (120 µs), and the minimum tool wear ratio obtained when using the copper electrodes and the kerosene dielectric alone reaches the values (0.31%) and (1.05%) when using the graphite electrodes with pulse current (22 A), pulse on duration (120 µs) and the kerosene dielectric, as shown in figure (9) and (10), respectively. The main conclusion of the TWR calculation process is that the best minimum value obtained when using the pulse current (8 A), the pulse on duration (120 µs), the copper electrode and the kerosene dielectric alone reaches the values (0.31 %) and experimentally reaches the values (0.42 %). In all cases, the use of abrasive powder mixing like graphite increases the tool wear ratio but at the same time increasing the material removal rates up to (271%) as indicated in table (5), experiment (28) comparing with experiments (1, 18 and 42) which given the best MRR values with the same high levels of input parameters, but with copper electrodes and without using the graphite powder mixing. The use of short pulse on time duration of (40 µs) and the low values of the used pulse current (8 A) will reduce the electrode wear ratio to its middle levels specially when using the kerosene dielectric alone. These wear levels are highly increasing when use the graphite electrodes and graphite powder mixing with the dielectric, because the efficiency of the material removal rates will be increasing due to high electrical and thermal conductivities as well as abrasive properties of graphite powder and the low density of graphite electrodes, where removal process will work efficiently even with low levels of thermal energy generated. The use of high current for a short time slightly increasing the tool wear ratio and the use of powder mixing improving the performance specially with using of graphite electrodes which will give a better wear ratios than with copper for both cases of using or not the graphite powder mixing because it transmits the generated heat away by increase the gap distance between the tool and the workpiece, because the addition of graphite powder to dielectric fluid would cause an increase in the electrical conductivity of the fluid thereby increasing the gap and then the abrasive and erosive processes will be working at a longer distance from the electrode surfaces. These tool wear ratios highly decrease with increasing the duration of pulse current time at the same values of used pulse current as shown in figures (9) and (10). These ratios are decreases to its minimum levels for all cases as indicated in figure(9), especially when using the kerosene dielectric alone specially when using the copper electrodes due to its high density as well as because the thermal conductivity of copper is less than graphite material which reduces the transition of thermal energy generated by the dielectric and this will reduce the ratio of carbon atoms interact with the electrode surface which is the main reason Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 29 to its wear. These minimum TWR levels will allow to working with longer machining times for a greater amount of metal removal with the minimum electrode wear. It also allows access to the best accuracy for parts, especially when machining parts of large depths by using the same elect rode without the need to be replaced, because the tool can maintain its original form for the longest period with these few percentage of wear ratios. 3.2.1 Numerical Optimization of Tool Wear Ratio Results For optimization the predicted model with the best EDM and PMEDM parameters, the desirability for minimize the values of response with the same ranges of the selected EDM parameters and electrodes types, as mentioned in table (10). The best solution founded from the desirability process shows that the optimum predicted values of the TWR when using the copper electrodes with pulse current (8.186 A), pulse of duration is (118.875 µs) and using the kerosene dielectric alone gives the best minimum predicted TWR of (0.364%) with a maximum desirability ratio (1.000). The desirability process reveals that the best predicting response values are approximately the same to the obtained values by experiments, and this confirms the results obtained experimentally. Table (10) indicated also that the use of graphite electrodes with pulse current (22 A), pulse of duration (120 µs) and using the kerosene dielectric alone gives also a good minimum predicted value of TWR of (1.059%) with a maximum desirability ratio (0.949). Table 9, The (ANOVA) analysis for material removal rate (TWR) after the EDM. Source Sum of Squares df Mean Square F Value p-value Prob> F Block 8.52 2 4.26 Model 399.00 10 39.90 139.45 < 0.0001 significant A-type of electrode 43.82 1 43.82 153.14 < 0.0001 B-Pulse current (Ip) 37.82 1 37.82 132.20 < 0.0001 C-Pulse on duration (Ton) 98.54 1 98.54 344.40 < 0.0001 D- Graphite powder mixed in kerosene dielectric 67.99 1 67.99 237.63 < 0.0001 AB 29.12 1 29.12 101.79 < 0.0001 AC 1.12 1 1.12 3.91 0.0572 AD 25.22 1 25.22 88.14 < 0.0001 BD 33.43 1 33.43 116.85 < 0.0001 ABC 1.50 1 1.50 5.24 0.0293 ABD 8.13 1 8.13 28.43 < 0.0001 Residual 8.58 30 0.29 Cor Total 416.10 42 Fig. 7. The 3D graphs for TWR using the pulse current (8 A) and pulse on duration (40 µs). Design points below predicted value X2 = D: SiC powder mixed in kerosene dielectric 0 5 Copper Graphite 0 2 4 6 8 10 12 14 T oo l w ea r ra tio (E W R ) (% ) A: type of electrode (-) D:Graphite powder mixed in kerosene dielectric (g/l) Design-Expert® Software Factor Coding: Actual Tool wear ratio(EWR) (%) Design points above predicted value Design points below predicted value X1 = A: type of electrode X2 = D: SiC powder mixed in kerosene dielectric Actual Factors B: Pulse current (Ip) = 8 C: Pulse on duration (T on) = 40 T oo l w ea r ra tio (E W R ) (% ) Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 30 Fig. 8. The 3D graphs for TWR using the pulse current (22 A) and pulse on duration (40 µs). Fig. 9. The 3D graphs for TWR using the pulse current (8 A) and pulse on duration (120 µs). Fig. 10. The 3D graphs for TWR using the pulse current (22 A) and pulse on duration (120 µs). Table 10, The desirability Process for optimization of the predicted TWR. X2 = D: SiC powder mixed in kerosene dielectric 0 5 Copper Graphite 0 2 4 6 8 10 12 14 T oo l w ea r ra tio (E W R ) (% ) A: type of electrode (-) D:Graphite powder mixed in kerosene dielectric (g/l) X2 = D: SiC powder mixed in kerosene dielectric 0 5 Copper Graphite 0 2 4 6 8 10 12 14 T oo l w ea r ra tio (E W R ) (% ) A: type of electrode (-) D:Graphite powder mixed in kerosene dielectric (g/l) Design points below predicted value X2 = D: SiC powder mixed in kerosene dielectric 0 5 Copper Graphite 0 2 4 6 8 10 12 14 T oo l w ea r ra tio (E W R ) (% ) A: type of electrode (-) D:Graphite powder mixed in kerosene dielectric (g/l) No. Type of electrode Pulse current (Ip) (A) Pulse on duration (Ton) (µs) SiC powder mixed in kerosene dielectric (gm/l) Tool wear ratio (EWR) (%) Desirability 1 Copper 8.186 118.875 0 0.364 1.000 Selected 37 Graphite 22.000 120.000 0 1.059 0.949 Design-Expert® Software Factor Coding: Actual Tool wear ratio(EWR) (%) Design points above predicted value Design points below predicted value X1 = A: type of electrode X2 = D: SiC powder mixed in kerosene dielectric Actual Factors B: Pulse current (Ip) = 22 C: Pulse on duration (T on) = 40 T oo l w ea r ra tio (E W R ) (% ) Design-Expert® Software Factor Coding: Actual Tool wear ratio(EWR) (%) Design points above predicted value Design points below predicted value X1 = A: type of electrode X2 = D: SiC powder mixed in kerosene dielectric Actual Factors B: Pulse current (Ip) = 8 C: Pulse on duration (T on) = 120 T oo l w ea r ra tio (E W R ) (% ) Design-Expert® Software Factor Coding: Actual Tool wear ratio(EWR) (%) Design points above predicted value Design points below predicted value X1 = A: type of electrode X2 = D: SiC powder mixed in kerosene dielectric Actual Factors B: Pulse current (Ip) = 22 C: Pulse on duration (T on) = 120 Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 31 3.3 Modeling of Surface Roughness Using Copper and Graphite Electrodes The ANOVA technique for the surface roughness (SR) response which are given in table(11) using the three factor backward levels for transform inverse and Partial sum of squares model for lower the p-value. The Model F-value of 461.55 implies the model is significant. Values of "Prob> F" less than 0.0500 indicate model terms are significant. In this case, A, B, C, D, AB, AC, BC, BD, CD, ABC, ABD, ACD, BCD are significant model terms. The predicted final empirical equation is: 1/(Surface roughness (SR)) = + 0.40985 +0.11090 * A - 7.40310 E –003 * B – 1.40639 E-003 * C + 0.043313 * D - 3.47700E-003 * A* B- 1.10998E–003 * A * C + 3.35689E-005 * B *C-2.87530E-003*B*D-5.70120E*D+ 3.63772 E- 005*A*B*C-4.80040E-004*A*B*D+9.13928 E- 005*A*C*D+3.09331E-005*B*C*D …(3) The three dimensional (3D) graphs given in figures (11 - 14) show the influence of the EDM and PMEDM parameters on the surface roughness. As shown in these figures, the minimum surface roughness (SR) values obtained when using the pulse current (8 A) and pulse on duration (40 µs) in all cases of the designed experimental groups. Figure (11) indicates that when using the copper electrodes and the kerosene dielectric alone, the minimum surface roughness reduced to (4.0470 µm), experimentally (3.91 µm). When using the graphite electrodes figure (13), the minimum surface roughness reduced to values (2.8723 µm), experimentally (2.81 µm). This means that the surface roughness improved by (139 %) because the graphite powder mixing owns a high electrical conductivity which will working on increasing the dielectric electrical conductivity and consequently the gap distance increases, then the pressurized dielectric from both sides will remove the new fine removal layers from the surface of the workpiece and take them to the outside of the gap area combining operation of evaporation and melting leaving a fine surface quality. Figure (12) and (14) show the 3D graphs for SR using the copper and graphite electrodes and graphite powder mixed with kerosene dielectric, where the minimum surface roughness reaches the values (3.8128 µm) and (2.7579 µm) respectively, experimentally with values (3.77 µm) and (2.77 µm), respectively. This means that the overall predicted minimum SR for all experiments runs obtained when working with graphite electrodes, the pulse current (8 A), the pulse on duration (40.µs) and kerosene dielectric mixed with graphite powder with (2.76µm) value, experimentally (2.77 µm), i.e., the process improved by (139 %), while experimentally improved by (141 %). In general, it is better to use the graphite electrodes because it's thermal and electrical conductivity are less than copper materials in many levels, thus it will produce a little value of discharge energy works to minimize the defects resulting from increased discharge energy such as electromechanical pits and decay formation which keeps the producing surfaces with higher quality and fine roughness. The use of graphite electrodes gives better surface roughness when using low pulse current levels for a small period of time, because the abrasion process cannot accomplish its work completely due to the little amount of thermal energy necessary for melting the surface layer of workpiece, and thus the abrasive phenomenon will be works with less abilities required to remove the surface layers as well as the lack of interactions required for the generation of new carbides due to low level of energy generated. The formation of a molten layer that freezes on the surface which is of better roughness than the erosive surfaces. Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 32 Table 11, The (ANOVA) analysis for (SR) after the EDM. Source Sum of Squares df Mean Square F Value p-value Prob> F Block 7.328E-004 2 3.664E-004 Model 0.14 13 0.011 461.55 < 0.0001 significant A-type of electrode 0.018 1 0.018 756.53 < 0.0001 B-Pulse current (Ip) 0.020 1 0.020 830.94 < 0.0001 C-Pulse on duration (Ton) 0.025 1 0.025 1039.58 < 0.0001 D-Graphite powder mixed in kerosene dielectric 2.766E-003 1 2.766E-003 114.75 < 0.0001 AB 4.366E-003 1 4.366E-003 181.13 < 0.0001 AC 0.016 1 0.016 645.36 < 0.0001 BC 3.657E-003 1 3.657E-003 151.72 < 0.0001 BD 2.926E-003 1 2.926E-003 121.39 < 0.0001 CD 4.091E-003 1 4.091E-003 169.70 < 0.0001 ABC 4.263E-003 1 4.263E-003 176.87 < 0.0001 ABD 7.877E-004 1 7.877E-004 32.68 < 0.0001 ACD 9.709E-004 1 9.709E-004 40.28 < 0.0001 BCD 3.044E-003 1 3.044E-003 126.30 < 0.0001 Residual 6.508E-004 27 2.410E-005 Cor Total 0.15 42 Fig. 11. The 3D graphs for SR using the copper electrodes and kerosene dielectric alone. Fig. 12. The 3D graphs for SR using the copper electrodes and (5g/l) graphite powder mixing in kerosene dielectric D: SiC powder mixed in kerosene dielectric = 0 40 60 80 100 120 8 10 12 14 16 18 20 22 2 3 4 5 6 7 S ur fa ce r ou gh ne ss ( S R ) (µ m ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) 40 60 80 100 120 8 10 12 14 16 18 20 22 2 3 4 5 6 7 S ur fa ce r ou gh ne ss ( S R ) (µ m ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) Design-Expert® Software Factor Coding: Actual Original Scale Surface roughness (SR) (µm) Design points above predicted value Design points below predicted value 6.32 2.77 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Copper Design-Expert® Software Factor Coding: Actual Original Scale Surface roughness (SR) (µm) Design points above predicted value Design points below predicted value 6.32 2.77 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Copper D: SiC powder mixed in kerosene dielectric = 5 Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 33 Fig. 13. The 3D graphs for SR using the graphite electrodes and kerosene dielectric alone. Fig. 14. The 3D graphs for SR using the the graphite and (5g/l) graphite powder mixing in kerosene dielectric. 3.3.1. Numerical Optimization of Surface Roughness Results For optimization the predicted model with the best EDM and PMEDM parameters, the desirability for minimize the values of response with the same ranges of the selected EDM parameters and electrodes types as mentioned in table (12). The best SR when using the graphite electrodes with pulse solution founded from the desirability process shows that the optimization predicted values of the current (8.009 A), pulse of duration about (40.408 µs) and using the kerosene dielectric with graphite powder mixed gives the best minimum predicted SR of (2.765 µm) with a maximum desirability ratio (1.000). The desirability process exhibits that the best predicting response values are approximately the same with the obtained values by experiments (2.77 µm), with a difference less than (0.015 µm), and this confirms the results obtained experimentally. Table 12, The desirability process for optimization of the predicted SR. Number Type of electrode Pulse current (Ip) Pulse on duration (T on) SiC powder mixed in kerosene dielectric Surface roughness (SR) Desirability 1 Graphite 8.009 40.408 5 2.765 1.000 Selected 2 Graphite 8.045 40.038 5 2.762 1.000 3 Graphite 8.063 40.401 5 2.769 1.000 40 60 80 100 120 8 10 12 14 16 18 20 22 2 3 4 5 6 7 S ur fa ce r ou gh ne ss ( S R ) (µ m ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) D: SiC powder mixed in kerosene dielectric = 5 40 60 80 100 120 8 10 12 14 16 18 20 22 2 3 4 5 6 7 S ur fa ce r ou gh ne ss ( S R ) (µ m ) B: Pulse current (Ip) ((A))C: Pulse on duration (T on) ((µs)) Design-Expert® Software Factor Coding: Actual Original Scale Surface roughness (SR) (µm) Design points above predicted value Design points below predicted value 6.32 2.77 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Graphite D: SiC powder mixed in kerosene dielectric = 0 Design-Expert® Software Factor Coding: Actual Original Scale Surface roughness (SR) (µm) Design points above predicted value Design points below predicted value 6.32 2.77 X1 = B: Pulse current (Ip) X2 = C: Pulse on duration (T on) Actual Factors A: type of electrode = Graphite D: SiC powder mixed in kerosene dielectric = 5 Ahmed N. Al-Khazraji Al-Khwarizmi Engineering Journal, Vol. 11, No. 3, P.P. 20- 36 (2015) 34 4. Conclusions The main conclusions obtained can be summarized in the following: 1- The best results for the productivity of the process (MRR) obtained when using the graphite electrodes, the pulse current (22 A), the pulse on duration (120 µs) and using the graphite powder mixing in kerosene dielectric reaches (82.84 mm³/min).This result gives an improvement in the material removal rate by (274%) with respect to the corresponding value obtained when using the copper electrodes with kerosene dielectric alone. 2- The best results for the tool wear ratio (TWR) of the process obtained when using the copper electrodes, the pulse current (8 A), the pulse on duration (120 µs) and using the kerosene dielectric alone reaches (0.31 %). 3- The use of graphite electrodes, the kerosene dielectric with graphite powder mixing, the pulse current (8 A) and the pulse on duration (40 µs) gives the best surface roughness (SR) of a value (2.77 µm). This result yields an improvement in SR by (141%) compared with using the corresponding value obtained when using copper electrodes, the kerosene dielectric alone and the same other parameters and machining conditions. The desirability process showed that the best predicting response values are approximately the same as to those obtained values by experiments, as mentioned in the three above items, and this confirms the results of the present work. Nomenclature ANOVA Analysis of variance CCD Central Composite Design CNC Computer numerical control EDM Electric discharge machining FFD Full factorial design Ip Pulse current (A) MRR Material removal rate (mm³/min) PMEDM Powder mixing electric discharge machining RSM Response surface methodology SR Surface roughness (µs) TIG Tungsten inert gas Ton Pulse on duration time (µs) Toff Pulse off duration time (µs) TWR Tool wear ratio (%) Vp Gap voltage (V) WEDM Wire electrical discharge machine w/p Workpiece 5. References [1] J. W. Murray, J. C. 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[17] ASTM A681, “Standard Specification for Tool Steels Alloy”, American Society for Testing and Materials, Washington, D.C., 1976. [18] ASTM A370,“Standard Test Method and Definitions for Mechanical Testing of Steel Products”,American Society for Testing and Materials,Washington, D.C., 1977. )2015( 20- 36، ���� 3، ا�� د11 ا���ارز� ا��� �� ا������� � ا��د �� ف ا��زر�� 36 � ن��ج�و درا ���� � ��!"#� �3"!2ا� 0�1 ا���/وج ا���ا-�, �+��قا�(&)�' #"�&�ارة ا�$� �+�!� ������� ا� �1 ا��4 *** � ����د �1 ��� ** *ا�7 ن"56 ا��/رج ����������/ ()' ا!&%$#� ا! *+!+%�ا!/�.-� ا!, **،* �� / 2345ء*�.-� ������()' ا!&%$#� ا! *** dr_ahmed53 @yahoo.com: 8!647$ اا��,4و!: * alrabiee2002@yahoo.com: 8!647$ اا��,4و!: ** smaengg@yahoo.com: 8!647$ اا��,4و!: *** ا����8 �4 $را#;;�3 ا!7?;;< اھ;;>6-%;;8 AB;;C ق+?;;(. E;;�F4ويا! ا!/4ا;;���4و#;;�;;. L�I;; 8;;FJKوج ا! ��;;� -;;�زلا! ا!K�34&5 2ل;;O P�Q;;R,!ا �;;�S TJUV!�;;3 ;;3 W64X,!� 8K�34&���و ا!�34+ن T�PMEDM ( ZS[ JUV!8!( )?+قا! J.YSO ا!!�T 4وم��+ع ا! D2 . 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