ريم و وسام60- 69 Al-Khwarizmi Engineering Journal, Vo Investigation the Optimization Roughness in Free Form Surface Reem S. Kazaal* *Department of Production Engineering and **Department (Received https://doi.org/10.22153/kej.2019.10.001 Abstract The aim of this research is to investigation the optimization of the machining parameters (spindle speed, feed rate, depth of cut, diameter of cutter and number of flutes of cutter) material (Aluminum 6061 reinforced boron carbide) the free-form surface. Side milling (profile) purpose of using ANFIS to obtain the better prediction of surface value and get optimum machining parameters by using Taguchi method 4500 r.p.m, 920mm/rev feed rate, 0.6mm depth of cut, 10mm diameter, 2 flute Keywords: Optimization, surface roughness, 1. Introduction Work has been done on composite material to achieve the best surface roughness relative to free form surfaces using end mill cutter on the side of the form by using vertical CNC milling machine. Mustafa Kurt et al. [1] for the purpose of pleasing customers, many free surface products are designed to enhance their aesthetic these products can have complicated surfaces to meet functional requirements, which necessitate specific aerodynamic, optical, medical, structural and processing characteristics. addressed the effects of factors on surface roughness and dimensional machining errors during free-form surface machining using experimental works using a ball-end mill in a 3 axis CNC milling machine. J.-M. Redonnet [2] End-milling of free-form surfaces on 5 NC machine tools is a complex problem non-convex surfaces interference in end milling Khwarizmi Engineering Journal, Vol. 15, No. 2, June , (2019) P.P. 60- 69 e Optimization of Machining Parameters n Free Form Surface of Composite Material Kazaal* Wisam K. Hamdan** *Department of Production Engineering and Metallurgy/ University of Techno *Department of Biomedical Engineering / University of Technology *Email: sabahreem18@yahoo.com **Email: wisamuot@yahoo.com (Received 16 July 2018; accepted 31 October 2018) https://doi.org/10.22153/kej.2019.10.001 The aim of this research is to investigation the optimization of the machining parameters (spindle speed, feed rate, depth of cut, diameter of cutter and number of flutes of cutter) of surface roughness for free- material (Aluminum 6061 reinforced boron carbide) by using HSS uncoated flat end mill cutters which are rare use of form surface. Side milling (profile) is the method used in this study by CNC vertical milling purpose of using ANFIS to obtain the better prediction of surface roughness values and decreased of the error prediction value and get optimum machining parameters by using Taguchi method for the best surface roughness 920mm/rev feed rate, 0.6mm depth of cut, 10mm diameter, 2 flute. ptimization, surface roughness, free-form surface, composite material, Taguchi method, milling machine. Work has been done on composite material to relative to free cutter on the side of the form by using vertical CNC milling machine. the purpose of pleasing customers, many free surface products are designed to enhance their aesthetic appearance; have complicated surfaces to which necessitate specific aerodynamic, optical, medical, structural This study addressed the effects of factors on surface ional machining errors form surface machining using end mill in a 3- M. Redonnet et al. form surfaces on 5-axis is a complex problem when in end milling which causes a risk between surface the research based on which turn works to locate the tool and thus improve the position of the tool relative to the goals set for each probability [3] the goal behind this study that the specific type of iso-planar milling for both roughing and finishing, has developed a process algorithm an optimal surface for freeform surface. While the algorithm is proposed solution for milling, it can be easily appropriate to other types of milling strategy such as contour milling. Both computer simulation and physical cutting experiments of the suggested method have convincingly demonstrated its makings over the traditional simple offset method. milling method with variable parameters suc speed, feed rate and diameter of cutter roughness more homogeneous with greater productivity relative to free form on the roughness measurement Al-Khwarizmi Engineering Journal Machining Parameters to Surface f Composite Material Hamdan** University of Technology University of Technology The aim of this research is to investigation the optimization of the machining parameters (spindle speed, feed rate, -form surface of composite flat end mill cutters which are rare use of vertical milling machine. The roughness values and decreased of the error prediction for the best surface roughness at spindle speed form surface, composite material, Taguchi method, milling machine. between the tool and the the research based on the algorithms which turn works to locate the tool and thus improve the position of the tool relative to the ability. Lufeng CHEN et al. the goal behind this study that the specific type planar milling for both roughing and finishing, has developed a process algorithm an optimal surface for freeform surface. While the algorithm is proposed solution for iso-planar milling, it can be easily appropriate to other types of milling strategy such as contour milling. Both computer simulation and physical cutting experiments of the suggested method have convincingly demonstrated its makings over the simple offset method. Rybicki [4] a milling method with variable parameters such as eed rate and diameter of cutter for surface roughness more homogeneous with greater productivity relative to free form surface. Based measurement results for the Reem S. Kazaal experiments, many problems have been set necessity of the surface curvature pre from primary profile, necessity of 2D roughness assessment in two perpendicular directions Shaghayegh Shajari et al. [5] discussed the effect of employing different cutter path strategies on cutting force, surface texture, and machining time when ball end milling of low curv surface; have shown that the use of different cutter path strategies when finishing ball end milling of low curvature convex surfaces has more effects on the cutting forces, surface texture, and machining time. In general, radial cutter path appears will give its preferred results with respect to the homogenous surface composition and obtain less cutting forces. Rodrigo Henriques Lopes da Silva et al. [6] using a method tool path strategies on side milling with a vertical end mill. The main objective of this study is to evaluate the path’s effect on the cutting forces and roughness of the surface, and simultaneously the effect of milling parameters (cutting speed, feed, and path period). Then apply experiments randomly and a Taguchi Method was applied and concludes that the trajectories of the tool significantly surface roughness. Istvan SZALOKI studied surface roughness (Ra, Rz, Rt, Rp, RSm and effect feed on the material (aluminum metal matrix composite) at constant speed with three value of feed per tooth. the search results proved that the Rz and Rp parameters are more able to give the influence of the milling parameters on the quality of the surface. The aim from these studies to obtain the best parameters to less roughness for convex region in free form surface. 2. Experimental Equipments 2.1 Materials Composite material (aluminum reinforcement with 4% boron carbide) used in this work and prepared by stir casting method metal matrix composite express one of the significant composite because have various applications like automotive and aerospace industries due to superior properties with conventional alloy [8]. This m been inspected in general company for engineering inspection and rehabilitation (S.I.E.R)/ (Lab. & E.I.Dep.) for chemical composition shown in table (1). Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 61 ny problems have been set: necessity of the surface curvature pre-filtering from primary profile, necessity of 2D roughness assessment in two perpendicular directions. discussed the effect ploying different cutter path strategies on cutting force, surface texture, and machining time when ball end milling of low curvature convex have shown that the use of different cutter path strategies when finishing ball end milling of re convex surfaces has more effects on the cutting forces, surface texture, and machining time. In general, radial cutter path appears will give its preferred results with respect to the homogenous surface composition and obtain less o Henriques Lopes da Silva method tool path strategies on side milling with a vertical end mill. The main objective of this study is to evaluate the path’s effect on the cutting forces and roughness of the surface, and simultaneously the effect of milling feed, and path period). Then apply experiments randomly and a Taguchi Method was applied and concludes that the trajectories of the tool significantly effect on Istvan SZALOKI et al. [7] Ra, Rz, Rt, Rp, RSm) on the material (aluminum metal matrix composite) at constant speed with three the search results proved that the Rz and Rp parameters are more able to give the influence of the milling parameters on the The aim from these studies to obtain the best parameters to less roughness for aluminumT-6061 reinforcement with 4% boron carbide) used in this and prepared by stir casting method. The metal matrix composite express one of the because have various applications like automotive and aerospace industries due to superior properties compared his material has been inspected in general company for engineering inspection and rehabilitation Lab. & E.I.Dep.) for chemical Table 1, Shown the chemical composition of (Al+B4C) composite material. elements % elements Si 0.756 Cr Fe 0.422 Ni Cu 0.309 Zn Mn 0.060 Pb Mg 0.88 Ti V 0.008 B4C Al Bal. 2.2 Machining CNC vertical milling BASED machine has been used to machining the composite material (Al+B4C) HSS end-mill cutter with different diameters (6, 8, and 10) mm; and number of flutes (2, 3, and 4).the machining (side milling) has been performed on free form surface shown in Fig (1 various parameters spindle speed, feed rate , depth of cut , diameter of cutter and number of flutes of cutter under dry cutting condition better roughness of the free form surface because of the rare use of this type of tool to machine this surface . Fig. 1. Shown the design of free form The dimensions of the form as shown in figure (2) Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) hown the chemical composition of (Al+B4C) elements % Cr 0.206 Ni 0.006 Zn 0.013 Pb 0.010 Ti 0.023 B4C 4% C-tek (KM-80 D) pc machine has been used to machining the composite material (Al+B4C) by using uncoated mill cutter with different diameters (6, 8, and 10) mm; and number of flutes (2, 3, and 4).the machining (side milling) has been performed on hown in Fig (1) dependent on various parameters spindle speed, feed rate , depth of cut , diameter of cutter and number of flutes of under dry cutting condition to obtain the better roughness of the free form surface because type of tool to machine this Shown the design of free form surface. The dimensions of the form as shown in figure (2). Reem S. Kazaal Fig. 2. Shown the dimension of the form. 3. Taguchi methods for designing experiments Taguchi has developed a system for the application testing designed; this system the design of experiments from the exclusive world of the statistician and making them more visible in the world of manufacturing contributions of the system also made the user simpler in obtaining fewer tests in experimental designs [9]. Taguchi submitted, the use of the loss function to measure the performance feature deviating from the required value, further convert into a signal-to-noise ratio. There are three the performance characteristic in the analysis of the signal-to-noise ratio (t better, the higher-the-better, and the nominal better) [10].in this study lower the better used to suggest of surface roughness in equation (1) � ���⁄ = �10 log ( ∑ ��� �� ) Where: � ���⁄ : signal to noise ratio ���= observed value of the ��� experiment at the ��� test. n = number of observation in a trials. Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 62 Fig. 2. Shown the dimension of the form. Taguchi methods for designing uchi has developed a system for the application testing designed; this system has taken the design of experiments from the exclusive and making them more visible in the world of manufacturing. The ibutions of the system also made the user simpler in obtaining fewer tests in experimental Taguchi submitted, the use of the loss function to measure the performance feature deviating further convert into a three types of the performance characteristic in the analysis noise ratio (the lower-the- better, and the nominal- the- lower the better was suggest of surface roughness in equation … (1) experiment at the s. 4. Surface Roughness Measurement Surface roughness was measured by using roughness measured device in Fig (3) for 18 experiments designed according to the Taguchi method orthogonal array with mixed levels� ��4 � � 1� used control parameters and their levels. Fig. 3. Roughness device (Marsurfps1). Table 2, The control parameters and their levels No. notation No.of level Low value 1 A 6 2500 2 B 3 120 3 C 3 0.2 4 D 3 6 5 E 3 2 When: A: spindle Speed, B: feed rate, c: depth of cut D: diameter of cutter, E: number of flutes The roughness has been measured for two areas (convex and concave) of free form surface shown in Fig (4). Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) Roughness Measurement Surface roughness was measured by using roughness measured device (MarSurfPS1) shown Fig (3) for 18 experiments designed according to the Taguchi method orthogonal array with ��; table (2) shows the parameters and their levels. Roughness device (Marsurfps1). he control parameters and their levels Low value High value unit 2500 5000 r.p.m 120 920 mm/rev 0.2 0.6 mm 6 10 mm 2 4 A: spindle Speed, B: feed rate, c: depth of cut D: diameter of cutter, E: number of flutes. The roughness has been measured for two areas (convex and concave) of free form surface Reem S. Kazaal Fig. 4. The model of the work. The results obtained for the values of the roughness shown in table (3) found in convex area less than concave area as the diagram (1) described below. Table 3, The roughness value in convex and concave areas No. A B C D E 1 2500 120 0.2 6 2 2 2500 520 0.4 8 3 3 2500 920 0.6 10 4 4 3000 120 0.2 8 3 5 3000 520 0.4 10 4 6 3000 920 0.6 6 2 7 3500 120 0.4 6 4 8 3500 520 0.6 8 2 9 3500 920 0.2 10 3 10 4000 120 0.6 10 3 11 4000 520 0.2 6 4 12 4000 920 0.4 8 2 13 4500 120 0.4 10 2 14 4500 520 0.6 6 3 15 4500 920 0.2 8 4 16 5000 120 0.6 8 4 17 5000 520 0.2 10 2 18 5000 920 0.4 6 3 Ra.1: the roughness in convex surface. Ra.2: the roughness in concave surface. Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 63 The results obtained for the values of the roughness shown in table (3) found in convex area less than concave area as the diagram (1) he roughness value in convex and concave areas Ra.1 Ra.2 1.123 1.423 4.510 5.140 1.660 1.128 4.091 5.130 0.966 1.081 2.052 1.650 2.745 3.166 1.038 1.067 2.214 2.27 2.581 3.592 4.576 3.462 1.436 1.312 1.063 1.073 1.506 1.568 1.076 1.424 1.381 1.590 0.806 1.145 1.747 4.159 Ra.1: the roughness in convex surface. Ra.2: the roughness in concave surface. Fig. 5. Shown the convex and concave 5. Prediction by (ANFIS) adaptive network based fuzzy inference system means a fuzzy inference system perform in the framework of adaptive networks. Soft computing oncoming including artificial neural networks and fuzzy inference have been used vastly to model expert attitude. Using given input and output data, the suggest ANFIS can put up mapping depended on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is utilize to model nonlinear functio most important factor of machine and predict it. More effective, faster response The program consists of eight types of membership functions; each type contains two components constant and linear function thus becoming 16 fuzzy rules. In this study ANFIS in matlab software program the surface roughness to obtain more accurate values after it was found that the error value was very few among the practical and predictive experiments; through the experiments co through the training and testing date found the triangular membership function type and input of membership function =2; type 200 shown in Figure (6) shown in table (4) The equation of error: Error= |��� !" #!" $%&'$()��$( ��� !" #!" $ 0 1 2 3 4 5 6 1 3 5 7 9 11 (Ra) value Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) Shown the convex and concave roughness. Prediction by (ANFIS) adaptive network based fuzzy inference system means a fuzzy inference system perform in the framework of adaptive networks. Soft computing oncoming including artificial neural networks and fuzzy inference have been used vastly to model ing given input and output data, the suggest ANFIS can put up mapping depended on both human knowledge (in the form of fuzzy then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is utilize to model nonlinear functions, to control the most important factor of machine and predict it. effective, faster response [11]. The program consists of eight types of membership functions; each type contains two components constant and linear function thus In this study ANFIS tool in matlab software program was used to predict the surface roughness to obtain more accurate values after it was found that the error value was very few among the practical and predictive experiments; through the experiments conducted through the training and testing date found the triangular membership function type and input of 2; type linear and epochs found that is less error &'$()��$( #!" $| � 100% ... (2) 11 13 15 17 experiments convex concave Reem S. Kazaal Fig. 6. Shown the membership function and epochs used for Table 4, Shown the error percentage. No. of experiments Actual value Predict value 1 1.123 1.12 2 4.510 4.51 3 1.660 1.66 4 4.091 4.09 5 0.966 0.967 6 2.052 2.05 7 2.745 2.75 8 1.038 1.04 9 2.214 2.21 6. Analysis of Variance (ANOVA) The ANOVA performed to check the statistical significance of the process factors affecting the surface roughness through by separating the total variability of the S/N ratios, can be calculated by the total squared deviations from the total mean of the S/N ratio, into contributions by each of the process parameters and the error. carried out to judge the significant parameter Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 64 . Shown the membership function and epochs used for predict. Error % No. of experiments Actual value 0.267 10 2.581 0 11 4.576 0 12 1.436 0.0244 13 1.063 0.103 14 1.506 0.0974 15 1.076 0.6711 16 1.381 0.192 17 0.806 0.1806 18 1.747 ariance (ANOVA) The ANOVA performed to check the statistical significance of the process factors affecting the surface roughness through by separating the total variability of the S/N ratios, can be calculated by the total squared deviations from the total mean of ratio, into contributions by each of the process parameters and the error. F-test was carried out to judge the significant parameter affecting the roughness. The larger F the ones that determine the most influential factor on performance. [12]. Analyzing the results using main effect plot and SN ratio to know the affect each paramet from low to high level Fig (7.a) and (7 main effect plot of surface roughness and for SN ratios in five Graphs and affect the chosen five machining parameters on surface roughness of free form surface. Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) Predict value Error % 2.58 0.0387 4.58 0.0874 1.44 0.278 1.06 0.282 1.50 0.398 1.08 0.3717 1.38 0.0724 0.808 0.2481 1.75 0.1717 affecting the roughness. The larger F-value was the ones that determine the most influential factor Analyzing the results using main effect plot and SN ratio to know the affect each parameter from low to high level Fig (7.a) and (7.b) shows main effect plot of surface roughness and for SN ratios in five Graphs and affect the chosen five ters on surface roughness of Reem S. Kazaal Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) 65 Fig. 7.a. Main effect plot for mean. Fig. 7.b. Main effect plot for SN ratio. The effect of combination parameters and how to influence on the surface roughness can be shown by the interaction plot shown in Fig (8). The minitab17 software program was used to create the ANOVA which illustrates the most effective factors to reduce roughness as shown in table (5). 5 00 0 4 50 0 4 00 0 3 50 0 3 00 0 2 50 0 3.0 2.5 2.0 1.5 1.0 92 0 52 0 12 0 0. 6 0. 4 0. 2 1 086 432 speed M e a n o f M e a n s feed doc dia flute Main Effects Plot for Means Data Means 50 00 45 00 40 00 35 00 30 00 25 00 -1 -2 -3 -4 -5 -6 -7 -8 -9 9 20 5 20 1 20 0. 6 0. 4 0. 2 1086 432 speed M e a n o f S N r a ti o s feed doc dia flute Main Effects Plot for SN ratios Data Means Signal-to-noise: Smaller is better Reem S. Kazaal Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) 66 Fig. 8. Interaction plot of parameters. Table 5, Analysis of variance results of convex region in free form surface Source DF Adj SS Adj MS F-Value P-Value Contribution Model 20 1084.06 54.203 59.21 0.000 Linear 5 638.98 127.796 139.60 0.000 A 1 527.78 527.779 576.55 0.000 82.597% B 1 16.29 16.287 17.79 0.000 2.549% C 1 29.15 29.153 31.85 0.000 4.561% D 1 68.66 68.662 75.01 0.000 10.745% E 1 2.53 2.529 2.76 0.097 0.395% Square 5 10.71 2.141 2.34 0.041 A*A 1 6.69 6.686 7.30 0.007 62.464% B*B 1 0.41 0.415 0.45 0.501 3.828% C*C 1 0.03 0.026 0.03 0.866 0.2801% D*D 1 2.90 2.899 3.17 0.076 27.077% E*E 1 0.67 0.675 0.74 0.391 6.255% 2-Way Interaction 10 438.73 43.873 47.93 0.000 A*B 1 4.96 4.964 5.42 0.020 1.1305% A*C 1 3.24 3.239 3.54 0.061 0.7384% A*D 1 7.90 7.902 8.63 0.003 1.8006% A*E 1 27.85 27.847 30.42 0.000 6.347% B*C 1 15.12 15.123 16.52 0.000 3.4463% B*D 1 152.38 152.378 166.46 0.000 34.731% B*E 1 8.00 7.998 8.74 0.003 1.823% C*D 1 0.43 0.429 0.47 0.494 0.0980% C*E 1 100.56 100.565 109.86 0.000 22.920% D*E 1 115.40 115.400 126.06 0.000 26.303% Error 465 425.67 0.915 Lack-of-Fit 455 425.67 0.936 * * Pure Error 10 0.000 0.000 Total 485 1509.73 100% Reem S. Kazaal Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) 67 7. Optimization by using Taguchi Method Taguchi methods of experimental design provide a simple, effective and systematic approach for the optimization of experimental design for performance quality and for expected economic production. This method is a unique and powerful statistical experimental design technique, which greatly improves the engineering productivity [10]. Through the roughness value in Table (3) shown Above that the roughness values in the convex region less than the concave region; therefore, the optimization has been performed in the convex region. Table 6, The parameters and their levels levels parameters A B C D E 1 1 1 1 1 2 2 2 2 2 3 4 3 3 3 3 5 6 Through the Table (7) can be obtained the mean of Ra.convex for SN. Table 7, mean of Ra for SN ratio level parameters A B C D E -6.198 -5.643 -5.988 -6.335 -2.209 -6.272 -5.365 -5.413 -5.157 -5.412 -8.208 -8.234 -1.721 -4.416 -4.327 -3.725 -5.055 -3.153 The large number was selected for all parameters in the table and take roughness values for the one level and divided on number of levels and applied in the equation (3) Ra=A5+B3+C3+D3+E1-(N-1) T ... (3) Where: N= number of parameters. T= the mean of the trails. Ra=0.7101+,. 8. The Result and Discussion The analysis of variance can be used to identify the effect of factors in reducing surface roughness and obtaining a highquality surface after showing the model summery in the Table (8) below; the convergence between practical R-Sq and predictive R-Sq (adj) experiments was very large and thus can be depended on the results of the analysis of variance (ANOVA) table. Table 8, Model summary. S R-sq R-sq (adj) R-sq (pred) 0.956772 71.81% 70.59% 68.61% Through the analysis of variance (ANOVA) table found the factor that affects a large amount to reduce the surface roughness in convex area of free form surface is spindle speed at 4500 r.p.m .This also has been found through main effect plot where the effect of influence 82.597%.Through the interaction when combination between two parameters found the fisher test value (F-value) at (B*D)=166.46 that represent a large value this means that feed rate and diameter of cutter are considered important parameters to less the surface roughness in convex area to free form surface where was the feed rate 920 mm/rev and (10 mm) diameter cutter were the best related roughness and its ratio 34.731% and it is fluent in diameter with flutes ;(2) flute express the better compared with other flutes . 9. Conclusion In this study shown: 1. The more influential parameters to obtain the surface roughness in free form surface at convex area in composite material are spindle speed (4500 r.p.m) and number of two flutes when the cutting area is constant and heat a few because it is a beginning to cut and not to be built up edge on the edge of cutter and we can conclude that the diameter of cutter when increase the surface roughness decreased. 2. From the optimization by Taguchi method found that the best machining parameters at spindle speed (4500 r.p.m), feed rate (920 mm/rev), depth of cut (0.6mm), diameter of cutter (10mm) and number of flute (2) to obtain the best surface roughness. Reem S. Kazaal Al-Khwarizmi Engineering Journal, Vol. 15, No. 2, P.P. 60- 69 (2019) 68 10. Reference [1] Mustafa Kurt, Selim Hartomacýoð, Bilçen Mutlu,Uður Köklü” Minimization of the surface roughness and for merror on the milling of free-form surfaces using a grey ralational analysis” Original scientific article,pp.205-213, 2012. [2] J.M. Redonnet, W. Rubio, F. Monies and G. Dessein” Optimising Tool Positioning for End-Mill Machining of Free-Form Surfaces on 5-Axis Machines for both Semi-Finishing and Finishing” International Journal Advancing Manufacturing Technol,2000. [3] Lufeng CHEN, Pengcheng HU, Ming luo, Kai TANG “Optimal interface surface determination for 4 multi- axis freeform surface machining with 5 both roughing and finishing”, Chinese Journal of Aeronautics, pp.15,2017. [4] M Rybicki” Problems during Milling and Roughness Registration of Free-form Surfaces”, Journal of Physics, 2014. [5] Shaghayegh Shajari, Mohammad Hossein Sadeghi, and Hamed Hassanpour,” The Influence of Tool Path Strategies on Cutting Force and Surface Texture during Ball End Milling of Low Curvature Convex Surfaces”, the Scientific World Journal, pp.14,2014. [6] Rodrigo Henriques Lopes da Silva & Amauri Hassui1” Cutting force and surface roughness depend on the tool path used in side milling: an experimental investigation”, The International Journal of Advanced Manufacturing Technology, 2018. [7] István SZALÓKI, Sándor SIPOS, Zsolt János VIHAROS “Aluminum-based MMC machining with carbide cutting tool” ICPM 2015 Congress 8th International Congress on Precision Machining, pp.151-156,2015. [8] S Jeyakumar, K Marimuthu &T Ramachandran “optimization of machining parameters of Al6061 composite to minimize the surface roughness-modelling using RSM and ANN’’Indian Journal of Engineering & Materials Sciences, Vol.22, pp.29-37,2014. [9] M. Nalbant, H.Go¨kkaya, G. Sur” Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning”, Materials and Design journal, 2007. [10] S.S.Chaudhari, S.S. Khedkar , N.B. Borkar,” Optimization of process parameters using Taguchi approach with minimum quantity lubrication for turning”, International Journal of Engineering Research and Applications (IJERA), Vol. 1, Issue 4, pp.1268-1273. [11] Navneet Walia, Harsukhpreet Singh, Anurag Sharma” ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey”, International Journal of Computer Applications, Vol.123, No.13, August 2015. [12] Kompan Chomsamutr, Somkiat Jongprasithporn” Optimization Parameters of tool life Model Using the Taguchi Approach and Response Surface Methodology”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, 2012. )2019( 60- 69، ! � 2د، ا�������15 ا���ارز � ا������� ا���� ر� ��ح ���� 69 '��&أ$ #�" � (�)� �����اد ا���01 ة.� ا�-,�ح ا���#� �ا�-, � ����ا � ا�+(*�� �� **و��م �0ظ ��3ان *ر� ��ح ���� =>.اد/ ا%#;اق/ ,ا%(8/9%678 ,ا%5"4# /123 ھ/.-, ا+*("ج وا%$#"دن* sabahreem18@gmail.com * .>;?%ا+%9(;و*@ا: wisamuot@yahoo.com :@*ا%?;<. ا+%9(;و** 7� �ا� 6AB4+ا C6DEF G%ا HE?%ا اIف ھ.K>, L6I<)%و ,4#.ل اNOD%ا C$P ط#و"D%ود ا.E%د ا.Pا+داة و ;O3, ا%#. ةا%$878د @Tة( *8MUA%, 6EO2%2? ,ا/%"=, .D#$%8ح اO2A%ة ?W;$%8اد ا$A%, ) 1 =9"ر=6. ا%?8رون ٦٠٦١-?96, ا%$/86مP.4 (-8=" ]6T;ا^?#6[ ط _>;`F NO3 ام ادوات.U)-ط, ا $T "K= L@ ھIه ا%(`;<_ ا%5"*?@ ھ@ ط;<1F L6"i4 ا%#d?@ ا%(8dEA% @`69ل GAP اLhT ا%(/?gات %16D ا%8MU* ,ا%;W, 6$3/, ا%(`;<_ ا%#$8د<"="-(U.ام 4 ,ا%.را- 4#.ل ،,دD63 /دوره ٤٥٠٠-78W"F ,D@ m*8Ml LhT, NO3 ,P;- ./P ]6EO<وا%(kOl c4 L6AD ا%(/?g وا%8dEل 8P GAPاL6I<)%دوره/14 ٩٢٠ ,ا، NOD%ا C$P٠.٦ 14، ا+داة ;O3١٠ 14،ط# ٢"D%ود ا.E%ا c4, .