<4D6963726F736F667420576F7264202D20E5E4CF20E6E3D1E6EC20DEC7D3E320E6C7CDE3CF31352D3235> This is an open access article under the CC BY license: Al-Khwarizmi Engineering Journal Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, September, (2022) P. P. 15- 25 Performance Prediction in EDM Process for Al 6061 Alloy Using Response Surface Methodology and Genetic Algorithm Hind Hadi Abdulridha* Marwa Qasim Ibraheem** Ahmed Ghazi Abdulameer*** *,**,***Department of Production Engineering and Metallurgy/ University of Technology/ Baghdad/ Iraq *Email: Hind.H.Abdulridha@uotechnology.edu.iq **Email: 70223@uotechnology.edu.iq ***Email: Ahmed.G.Taki@uotechnology.edu.iq )2022 10 August; Accepted 22027 March Received ( https://doi.org/10.22153/kej.2022.08.001 Abstract The Electric Discharge (EDM) method is a novel thermoelectric manufacturing technique in which materials are removed by a controlled spark erosion process between two electrodes immersed in a dielectric medium. Because of the difficulties of EDM, determining the optimum cutting parameters to improve cutting performance is extremely tough. As a result, optimizing operating parameters is a critical processing step, particularly for non-traditional machining process like EDM. Adequate selection of processing parameters for the EDM process does not provide ideal conditions, due to the unpredictable processing time required for a given function. Models of Multiple Regression and Genetic Algorithm are considered as effective methods for determining the optimal processing variables of Electrical Discharge Machining. The material removal rate (MRR) and tool wear (Tw) were investigated using the process variables of pulse on time (Ton), pulse off time (Toff), and current intensity (Ip). The established empirical models were used to perform Genetic Algorithm (GA) to maximize (MRR) and minimize (Tw). The optimization results were utilized to establish machining conditions, validate empirical models, and obtain optimization outcomes. The optimal result that appears in this work was the pulse on (176.261 μs), pulse off (39.42 μs), and current intensity (23.62 Amp.) to maximize the MRR to (0.78391 g/min) and reduce tool wear to (0.0451 g/min). Keywords: Electro Discharge Machining, Genetic Algorithm, MRR, Tool Wear. 1. Introduction Machining Aluminum alloy using traditional machining technologies has problems such as high cutting temperatures and a high tool wear ratio. Aluminum is employed in a variety of industries, including automobiles, and aerospace. Aluminum alloy, on the other hand, has some advantages due to its low cost, low density, availability, and manufacturability [1]. AA 6061 Aluminum alloy is a precipitation- hardened variant of the 6000 series Al alloys that are widely used. It's a heat-treatable extruded alloy with medium to high strength properties [2]. Electrical discharge machining (EDM) is a non-traditional machining technique that is commonly used to machine die surfaces [3]. EDM is a popular production technique for difficult-to-machine materials and complex geometries. Wire and electrode EDM are the two primary types of EDM processes. The manufacturing process setup includes the electrode material, the geometry of wire diameter or the electrode, and the energy transfer parameters of voltage V with its polarity, pulse current intensity I, and pulse on time (Ton) and pulse off time (Toff) [4][5]. Several authors attempted to machine several Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 16 materials by utilizing the electrical discharge machining process. S. Ranjith et al. [6] examined the influence of EDM machining variables (pulse-on duration, current, pulse-off duration, and spark gap voltage) on MRR and Tw of silicon nitride–titanium nitride ceramic composites with the copper electrode. From the results, it has been shown that the current is a highly important factor among other parameters on both MRR and TWR. Higher material removal rate is obtained when pulse-on time and current is higher, whereas lower EWR result from high gap voltage and low current. Huu- Phan et al. [7] applied Multi-response optimization based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to examine the impact of low- frequency vibration on material removal rate MRR and Surface Roughness Ra in EDM process. From the experimental results, in the low-frequency vibration aided EDM technique, the process performance accuracy has been enhanced to around 86.6 percent. By enhancing the quality of the machining surfaces, TOPSIS was able to improve the low-frequency vibration-assisted EDM process' performance. The material removal rate can be increased with low-frequency vibration due to the controlled spark energy. Mandeep and Sthitapragyan [8] examined the effects of machining parameters such as pulse-on time (T-on), pulse-off time (T- off), current (I), and voltage (V) on the MRR of Aluminum based composite material. The aluminum metal matrix composite was cast with 200 mesh size (Avg. size 75 mm) particles (20 percent SiCp and 8% Grp). Response surface methodology RSM created the design matrix and mathematical models. The experimental results indicate that the pulse-on time and current are both major factors that directly affected (increased) the material removal rate, according to the analysis. Finally, at a high level of "pulse-off time," the MRR is minimal, but "voltage" has no significant effect on MRR. Mandeep et al. [9] determined the optimum process parameters (Peak current (I), voltage (V), Ton, and tool material) that affect the MRR and Ra of EDM during the machining of a new hybrid aluminum metal matrix composite. From the analysis of variance (ANOVA), the MRR increased as the Ton and pulse current increased, however, the MRR declined as the voltage increased. Reduced Ra, on the other hand, could only be achieved with low I, V, and pulse length. It was also revealed that the electrodes used in EDM had a substantial impact on MRR and surface roughness. Chinmayee et al. [10] investigated the influence of input parameters (discharge current, pulse-on-time, and open circuit voltage) on MRR, Tw and radial overcut of AA 7075 aluminum- 6% red mud metal matrix composites (AMMC) in the EDM process. From the results, it has been observed that pulse on-time and discharge current have a crucial influence on machinability characteristics of (AMMC) by providing useful information with less deviation to improve the accuracy of the EDM parts. Ramanuj et al. [11] carried out an experimental investigation to study the effect of (voltage, pulse on time, and current) on the (Ra) and (MRR) of Ti-6Al-4V ELI using EDM. Multi response Grey Relation Analysis (GRA) technique has been utilized for optimizing the machining parameters. From the results, it has been presented that the MRR and Ra were directly proportional to discharge current. Sagar and Pravin [12] provided an experimental investigation on the effect of (pulse on duration, discharge voltage, capacitance, and electrode rotation speed) on side gap width, MRR, and taper ratio when drilling Titanium alloy (Ti6Al4V) with copper tungsten (CuW) electrode. The experimental results demonstrate that capacitance, discharge voltage, and electrode rotation speed all have an impact on MRR parameters, whereas pulse on time, capacitance, and electrode rotation speed influence side gap width. On the other hand, capacitance and pulse on duration were the affecting parameters on the taper ratio. R.Rajesh and M. Dev Anand [13] calculated the optimum operating parameters namely; working voltage, oil pressure, spark gap, Pulse On Time, and Pulse Off Time, affecting the MRR by developing genetic algorithm and multiple regression models with an empirical model by conducting experiments based on the Grey Relational Analysis, which were used to obtain the greatest value for MRR in electric discharge machine. Tzeng et al. [14] analyzed MRR, electrode wear ratio, and workpiece surface finish on process parameters during the manufacture of SKD61 by electrical discharge machining (EDM). A hybrid method including a back-propagation neural network (BPNN), a genetic algorithm (GA), and response surface methodology (RSM) were proposed to determine optimal parameter settings of the EDM process. Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 17 In this paper, the influence of EDM parameters (pulse on time Ton (μs), pulse off time Toff (μs) and current intensity Ip (A)) was studied to determine their influence on the Material Removal Rate (MRR) and Tool Wear Rate (Tw) values based on Response Surface Methodology (RSM) and Genetic algorithm. 2. Experimental Work A CHEMER EDM machine model (CM-323 C) as shown in Figure 1, is used to implement the experimental work. Fig. 1. EDM Tool Model CM 323C Fig. 2. Work piece Samples of Al-6061 Alloy 2.1 Work piece material For evaluating the optimum values of process variables, a Copper electrode was used to Machine twenty samples of AA 6061 Aluminum alloy with dimensions (15×15×5mm) as shown in Figure 2. Table 1 illustrates the Chemical Composition of AA 6061 Alloy. Table 1, Al-6061 Alloy Chemical Composition Sample Workpiece material Cu % 0.388 Fe % 0.195 Si% 0.49 Mg % 1.07 Mn % 0.068 Zn % 0.003 Cr% 0.243 Ti% 0.019 Al% Balance 2.2 Selection parameters and their levels Process variables are the parameters that influence Material Removal Rate (MRR) and Tool Wear Rate (Tw) of machined surface and include the current intensity (Ip), pulse on time (Ton), and pulse off time (Toff). Table 2 shows the parameters values and their levels that were used in the experiments. Table 2, parameters values and the levels used Process Parameters pulse on time Ton (μμμμs) pulse off time Toff (μμμμs) current intensity Ip (Amp.) Levels Low 100 25 8 Medium 150 37 16 High 200 50 24 2.3 Design and Analysis of Experimental Work To find which input parameters generate the optimum output and to identify the influence of input parameters that enhance the developed qualities of the part above the obtained qualities, RSM was adopted to in this study to develop statistical and mathematical models due to its reliable performance [15, 16]. The central composite design is the most frequent way of Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 18 building a quadratic model of a response surface (CCD). Experiments are conducted with RSM and a Central Composite Design (CCD) matrix with Two-level factorial (full factorial) where it consists of 8 cube points, 6 center points, and 6 axial points with α=1.68179. Experimental design is an important stage in creating a response surface model using MINITAB software[15]. Material Removal Rate (MRR) and Tool Wear Rate (Tw) are calculated from equations (1,2) [17, 18] after experimentation, as shown in Table 3. MRR = ���� ��� � ��� �� ��������� ���� ���� �� ��� ����� … (1) T� = ���� ��� � ��� �� ��� ���� ���� �� ��� ����� … (2) Table 3, Experimental results of (MRR), (Tw) Tw (g/min) MRR (g/min) Ip Toff Ton No. 0.0024 0.0790 8 25 100 1 0.0023 0.0820 8 25 200 2 0.0013 0.0700 8 50 100 3 0.0010 0.0760 8 50 200 4 0.0100 0.5040 24 25 100 5 0.0130 0.6890 24 25 200 6 0.0048 0.4952 24 50 100 7 0.0231 0.5300 24 50 200 8 0.0065 0.3050 16 37 100 9 0.0069 0.4160 16 37 200 10 0.0130 0.3845 16 25 150 11 0.0130 0.3540 16 50 150 12 0.0015 0.0810 8 37 150 13 0.0120 0.6300 24 37 150 14 0.0069 0.3800 16 37 150 15 0.0069 0.3800 16 37 150 16 0.0069 0.3800 16 37 150 17 0.0069 0.3800 16 37 150 18 0.0069 0.3800 16 37 150 19 0.0069 0.3800 16 37 150 20 3. Results and Discussion 3.1. Analysis of Variance On the basis of Table 3's experimental findings, the impact of the input variables (Ton), (Toff), and (Ip) on the outputs (MRR and Tw), is investigated using MINITAB 17 software and Analyses of Variance (ANOVA). The significance of the model is determined using ANOVA. The ANOVA results for MRR and Tw are shown in Tables 4-5, respectively. Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 19 Table 4, ANOVA of MRR Source DF Adj SS Adj MS F-Value P-Value Model 9 0.648477 0.072053 118.07 0.000 Linear 3 0.620442 0.206814 338.91 0.000 Ton 1 0.011445 0.011445 18.75 0.001 Toff 1 0.004550 0.004550 7.46 0.021 Ip 1 0.604448 0.604448 990.51 0.000 Square 3 0.015405 0.005135 8.41 0.004 Ton*Ton 1 0.001454 0.001454 2.38 0.154 Toff*Toff 1 0.000493 0.000493 0.81 0.390 Ip*Ip 1 0.002155 0.002155 3.53 0.090 2-Way Interaction 3 0.011331 0.003777 6.19 0.012 Ton*Toff 1 0.002771 0.002771 4.54 0.059 Ton*Ip 1 0.005555 0.005555 9.10 0.013 Toff*Ip 1 0.003005 0.003005 4.92 0.051 Error 10 0.006102 0.000610 Lack-of-Fit 5 0.006102 0.001220 * * Pure Error 5 0.000000 0.000000 Total 19 0.654580 S = 0.0247030, R-sq = 99.07%, R-sq(adj) = 98.23%, R-sq(pred) = 85.78% Fig. 3. MRR Main Effects Plot. Fig. 4. Tw Main Effects Plot 200150100 0.6 0.5 0.4 0.3 0.2 0.1 503725 24168 Ton M e a n Toff Ip Main Effects Plot for MRR Data Means 200150100 0.014 0.012 0.010 0.008 0.006 0.004 0.002 503725 24168 Ton M e a n Toff Ip Main Effects Plot for TWR Data Means Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 20 It is clear from the main effects plot of Figure 3 that the material removal rate highly increases with the increase of current intensity, and decreases with the increase in pulse on time and pulse off time, the reason behind this is that the discharge energy increases with the increase of pulse on time and peak current leading to a faster cutting rate. With the decrease in the pulse off time, the number of discharges within a given period becomes more which leads to a higher material removal rate. To depict the input variables relationship between (Ton, Toff, and Ip) and the output (MRR), Material Removal Rate mathematical model is established as in equation 3. MRR = -0.714 + 0.00350 Ton + 0.01230 Toff + 0.04211 Ip - 0.000009 Ton*Ton - 0.000086 Toff*Toff - 0.000437 Ip*Ip - 0.000030 Ton*Toff + 0.000066 Ton*Ip - 0.000194 Toff*Ip … (3) Table 4 shows the overall significance of the mathematical model, with (R-Sq) determining the fit value between predicted and experimental findings. The (R-Sq(adj)) value indicates that the independent variables (Ton, Toff, and Ip) recorded (98.23) percent of the variance in the dependent variable (Y), with the remainder due to random error. Table 5, ANOVA of Tw Source DF Adj SS Adj MS F-Value P-Value Model 9 0.000522 0.000058 7.41 0.002 Linear 3 0.000388 0.000129 16.52 0.000 Ton 1 0.000091 0.000091 11.61 0.007 Toff 1 0.000001 0.000001 0.08 0.783 Ip 1 0.000297 0.000297 37.88 0.000 Square 3 0.000042 0.000014 1.79 0.213 Ton*Ton 1 0.000001 0.000001 0.13 0.730 Toff*Toff 1 0.000018 0.000018 2.26 0.163 Ip*Ip 1 0.000038 0.000038 4.81 0.053 2-Way Interaction 3 0.000094 0.000031 3.98 0.042 Ton*Toff 1 0.000028 0.000028 3.58 0.088 Ton*Ip 1 0.000059 0.000059 7.52 0.021 Toff*Ip 1 0.000007 0.000007 0.85 0.377 Error 10 0.000078 0.000008 Lack-of-Fit 5 0.000078 0.000016 * * Pure Error 5 0.000000 0.000000 Total 19 0.000600 S = 0.0027986, R-sq = 86.96% , R-sq(adj) = 75.22%, R-sq(pred) = 0.00% It is also clear from the main effects plot of Figure 4 that the most influencing factor on tool wear rate is the peak current; tool wear rate is minimum at lower currents. To depict the input parameters relationship between (Ton, Toff, and Ip) and the output (Tw), Tool Wear Rate mathematical model is established as in equation 4. Tw = 0.0397 - 0.000232 Ton - 0.001797 Toff + 0.001171 Ip + 0.000000 Ton*Ton + 0.000016 Toff*Toff - 0.000058 Ip*Ip + 0.000003 Ton*Toff + 0.000007 Ton*Ip + 0.000009 Toff*Ip … (4) Table 5 shows the overall significance of the mathematical model, with (R-Sq) determining the fit value between predicted and experimental findings. The (R-Sq(adj)) value indicates that the independent variables (Ton, Toff, and Ip) recorded (75.22) percent of the variance in the dependent variable (Y), with the remainder due to random error. Figure 4 shows the Tw Main Effects Plot. Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 21 3.2 GA Results Genetic algorithm is a probabilistic search technique that generates a new population from an iterative collection (called a population) of mathematical objects (typically fixed-length binary character Strings), each with a fitness value [19]. The genetic algorithm analyzes the experimental and expected values by using the intersection technique and the best value is supplied in the schedule below. Genetic Algorithm Input Population size=50 Population type=double vector No. of generations = 50 No. of generations chosen = 15 Fitness Function =Rank scaling Cross function = Two-point Cross over Fraction of = 0.8 Mutation Function = adaptable Feasible The best results were obtained after choosing the fitness function for a total length of the string of 18 and then transferring the results to MATLAB after setting up GA parameters and presenting them in Table 6. Table 6, GA that results for MRR, tool wear No. Ton Toff Ip MRR Tw Rank 1 100.231 25.263 9.41 0.063425 0.0025 1 2 123.451 29.421 13.44 0.0736586 0.0031 1 3 110.275 35.781 17.23 0.0208128 0.0071 1 4 133.651 38.621 16.42 0.3721823 0.0097 1 5 144.573 38.689 18.931 0.46217 0.0187 1 6 176.261 39.42 23.623 0.78391 0.0451 1 7 157.621 33.465 12.217 0.11327 0.0037 1 8 167.951 43.26 19.425 0.53217 0.02321 1 9 172.651 46.72 15.621 0.27218 0.0110 1 10 155.621 35.621 17.631 0.43218 0.0132 1 11 143.679 42.631 18.965 0.49265 0.0211 1 12 175.692 47.345 22.31 0.61329 0.032 1 13 199.200 37.625 16.781 0.39781 0.0197 1 14 184.222 48.681 19.678 0.59232 0.0232 1 15 137.437 28.42 14.597 0.21379 0.0111 1 Fig. 5. The implemented data on GA Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 22 Table 7, The optimum solution for several values of GA parameters Number of iteration crossover Mutation Optimal solution Best mean 1 2 3 4 5 0.6 0.6 0.6 0.6 0.6 0.04 0.05 0.06 0.07 0.08 1.356321 1.366012 1.369412 1.477213 1.478231 1.356453 1.367432 1.373211 1.477631 1.478912 6 7 8 9 10 0.75 0.75 0.75 0.75 0.75 0.04 0.05 0.06 0.07 0.08 1.565221 1.572132 1.575423 1.579543 1.594352 1.566321 1.572322 1.576421 1.582311 1.596432 11 12 13 14 15 0.8 0.8 0.8 0.8 0.8 0.04 0.05 0.06 0.07 0.08 1.653211 1.663214 1.594231 1.694325 1.82274 1.662132 1.673421 1.594326 1.694321 1.82281 Providing better reproductive opportunities through offspring gives more possible solutions so Table 7 shows that the increase in the crossover value caused improvement in the results until it reaches the optimal values, where reading No (15) in table 7 showed the best fitness (1.82274) and mean fitness (1.82281) which was evident in the Figure 5 that represents the implementation of the program. The pulse on time, pulse off time, and current intensity were all optimized. The aim here is to reduce tool wear while increasing the rate of material removal. The following are the boundary conditions for the decision variables pulse on time, pulse off time, and current intensity: Pulse on time (Ton) = (100 to 200 μs) Pulse off time (Toff) = (25 to 50 μs) Current intensity (Ip) = (8 to 24 Amp.) Figures 6-7 illustrate the effect of (Toff) on MRR and tool wear, where its increase in Toff led to an increase in MRR to optimal value (0.78391 g/min) at Toff (39.42 μs) with a decrease in tool wear by (0.0451 g/min), while figures 8-9 indicate the effect of current intensity on MRR and Tw, respectively the optimal values was at Ip (23.623 Amp.). Fig. 6. Effect of pulse off time on MRR. Fig. 7. Effect of pulse off time on Tw. 0 20 40 60 1 3 5 7 9 11 13 15 T o ff ( μ s) Experiment No. Toff (μs) 0 20 40 60 1 3 5 7 9 11 13 15 T o ff ( μ s) Experiment No. Toff (μs) Tw (g/min) Hind Hadi Abdulridha Al-Khwarizmi Engineering Journal, Vol. 18, No. 3, P.P. 15- 25 (2022) 23 Fig. 8. Effect of current intensity on MRR. Fig. 9. Effect of current intensity on Tw. 4. Conclusions This research presents a realistic method for optimizing EDM cutting parameters based on GA. The machining parameters included pulse on, pulse off, and current intensity Ip. The EDM method yields results such as metal removal rate and tool wear. The response surface method (RSM) uses statistical and mathematical methods for issue modeling and analysis to locate the input variables that generate the optimum response. Finally, GA was able to determine the best circumstances. That is, between experimental data, pulse on (176.261 μs), pulse off (39.42 μ s), current intensity (23.62 Amp.) to maximize the MRR to (0.78391 g/min) and reduce tool wear to (0.0451 g/min). The machining current of the EDM process is the most influential factor revealed by the response table. 5. References [1] A. Elanthiraiyan, G. Antony, S. Ashok and S. Sathiyaraj,"Investigation of Machining Characteristics of Aluminum 8011 by Wire Cut EDM Process", Int. J. Chem. Sci.: 14(4), 3119-3130, 2016. [2] R. Sharma, P. Katyal, V. Gill, M. Gupta,"A Research on Investigating the Optimization of Process Parameters of Aluminum Alloy 6061 by using Wire EDM", IJRASET, ISSN: 2321-9653, 6 (II), 2018. [3] P. Srinivasa, K. Ramji and B. Satyanarayana, "Surface integrity of wire EDMed aluminum alloy: A comprehensive experimental investigation", journal of King Saud University – Engineering Sciences, 30 (4O), 368–376, 2016. [4] S. Marichamy, B. Stalin, M. Ravichandran and G. 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Senthil, "Optimization of Cutting Parameters in Turning of AA6351 Using Response Surface Methodology and Genetic Algorithm", IJAER, 10 (23), 43905- 43911,2015. )2022( 15-25، صفحة 3العدد ، 18مجلة الخوارزمي الهندسية المجلد فاطمة محمد جاسم 25 وتأكل المعدن ازالة بمعدل للتنبؤ االستجابة سطح منهجية مع الجينية الخوارزمية تقنية االداة في عملية التشغيل بالشرارة الكهربائية **مروى قاسم ابراهيم * هند هادي عبد الرضا ***احمد غازي عبد االمير *،**،*** قسم هندسة اإلنتاج والمعادن/ الجامعة التكنولوجية Hind.H.Abdulridha@uotechnology.edu.iqااللكتروني: *البريد 70223@uotechnology.edu.iq :البريد االلكتروني** Ahmed.G.Taki@uotechnology.edu.iq : ***البريد االلكتروني خالصة ال متحكم شرارة تكوين عملية طريق عن المواد إزالة فيها يتم جديدة ة كهر وحراري تصنيع تقنية هي) EDM( التشغيل بالشرارة الكهربائية طريقة . للغاية صعب أمر القطع أداء لتحسين القطع متغيرات أفضل تحديد فإن ، EDM صعوبات عملية بسبب . عازل وسط في مغمسين قطبين بين بها التحديد المناسب لمتغيرات إن . EDM لمث التقليدية غير ال لعمليات التشغيل خاصة حاسمة، خطوة التشغيل متغيرات امثل إيجاد يعد لذلك، نتيجة المتعدد االنحدار نماذج إنشاء تم . لصعوبة التنبؤ بوقت المعالجة المطلوب للمهمة المعطاة هذه العملية ربما ال يعطي الظروف المثلى نظرا المواد إزالة معدل التحقق من تم . المشكلة لحل هذه الكهربائي التفريغ معالجة في المثلى المعالجة متغيرات لتحديد فعالة كطرق جينيةال خوارزميةالو )MRR (األداة معدل بليانو )Tw (النبضةتشغيل زمن وهي: العملية متغيرات باستخدام )Ton،( النبضة إطفاءزمن و )Toff،( التيار وشدة )Ip .(الجينية الخوارزمية إلى المستند األهداف متعدد التحسين ألداء المحددة التجريبية النماذج استخدام تم )GA (لتعظيم )MRR ( وتقليل )Tw .( النتائج أظهرت .المخرجات المثلى على والحصول التجريبية، النماذج صحة من والتحقق اآللي، التشغيل ظروف إلنشاء األمثلية نتائج استخدام تم أدت للحصول على ) ٢٣٫٦٢.Amp وشدة التيار ( ، )μs٣٩٬٤٢ النبضة (إطفاء زمن ،)μs١٧٦٫٢٦١النبضة ( تشغيل زمن ان الظروف المثلى: .)g/min٠٫٠٤٥١ ( واقل معدل بليان أداة) g/min٠٫٧٨٣٩١ ( معدل إزالة مواد أعلى