82- 88 Al-Khwarizmi Engineering Journal,Vol. 11, No. 4, P.P. The Influence of Design and Technological Parameters on the MAF Saad kariem **Department of (Received Abstract Experimental work from Magnetic Abrasive Finishing (MAF) tests and number of cycle which are formed the cutting speed, working gap, and finishing in MAF process. This research has made to study the effect of design and technological parameters on the surface roughness (Ra), micro hardness (Hv) and material removal (MR planned using response surface methodology according to The analysis of variance and instruction curves indicate some significant. the surface roughness Ra for steel, X2 has mildly significant, while showed that roughness of workpiece decreased from 0.3 to 0.15 Keywords: Design and technological parameters software. 1. Introduction Non-traditional MAF process were method for technology of machining MAF was removed microchips, in order to get the higher mechanical properties surfaces, process has been used to produce microrelief layer. The specialty of MAF capability to control the flexibility ferromagnetic powder sealing by magnetic field one can control the density and rigidity of the magnetic brush, that help to topography of magnetic flux in the working gap This specialty of MAF process was finishing methods. MAF process was simplicity; improved the quality of roughness (Ra) above 50 %. MAF process, gives good economic and ecological environment [1-6], by this method can finished different surfaces like cylindrical, flat, bolt complex shapes, for ferromagnetic materials [ Khwarizmi Engineering Journal,Vol. 11, No. 4, P.P. 82- 88 (2015) Design and Technological Parameters on the MAF Process Saad kariem Shather* Shakir M. Mousa Department of Production Engineering/ University of Technology **Technical Institute/ Musayab *Email: drengsaad_k_sh@yahoo.com **Email: shakir.aljabiri_89@yahoo.com (Received 7 April 2015 ; accepted 16 June 2015) from Magnetic Abrasive Finishing (MAF) tests was carried out design parameters are formed the shape of electromagnetic pole), and technological parameters finishing time) all have an influence on the mechanical properties of the surface layer has made to study the effect of design and technological parameters on the surface and material removal (MR) in working zone. A set of experimental planned using response surface methodology according to Taguchi matrix (36) with three levels and instruction curves indicate some significant. X1; X4; X6 have a significant effect on 2 has mildly significant, while X3 and X5 have insignificant effect. creased from 0.3 to 0.15 µ m that means improved the roughness to technological parameters, micro-hardness, surface roughness, material of removal were advanced technology of machining, the ability of in order to get the surfaces, this to produce microrelief process was flexibility of tool, ferromagnetic powder sealing by magnetic field, one can control the density and rigidity of the change the in the working gap. differs at other was universal, the quality of surface . MAF effective and ecological this method can finished like cylindrical, flat, bolt, and materials [4]. This work aims to study and technological parameters surface MR, by using experimental finding the mathematical mo MINITAB software. 2. Experimental Procedure An electromagnetic inductor manufactured using for The inductor was a steel rod wrapped around a coil of wires, magnetic force working gap, between pole gap was filled with powder and the applied by (DC) power supply Al-Khwarizmi Engineering Journal (2015) Design and Technological Parameters on the MAF Mousa** University of Technology esign parameters (amplitude, ), and technological parameters (current, all have an influence on the mechanical properties of the surface layer has made to study the effect of design and technological parameters on the surface . A set of experimental tests has been with three levels and six factors. have a significant effect on 5 have insignificant effect. The results the roughness to 100%. material of removal, MINITAB This work aims to study the influence of design parameters on the quality of experimental method then finding the mathematical models with the rocedure An electromagnetic inductor has designed and finishing flat surfaces. a steel rod wrapped around a of wires, magnetic force was generate on the , between pole and workpiece, the gap was filled with powder and the current was supply. See Figure 1. Saad kariem Shather Al-Khwarizmi Engineering Journal, Vol. 11, No. 4, P.P. 82- 88(2015) 83 Fig. 1. Electromagnetic inductor. The characteristic of inductor are the following: - The materials of the core was low carbon steel C15 - The diameter of copper wire was 0.9mm - The number of turns was N=3000 turns - The material of pole from low carbon steel - The abrasive powder was (65%) oxide of the iron with (35%) tungsten carbide. - The doze of powder was (5 cm3). There are six input parameters have been choose, the values of parameters and their three levels were illustrate in Table (1). The input parameters were applied according to the Taguchi matrix (L27) and the output was shown in Table (2). Three observed value of change in surface roughness (Ra), weight (MR),and micro-hardness (Hv), were examined, for ferromagnetic material as steel 304, before and after polishing measured the Ra, Hv and, w then finding the value ∆Ra, ∆Hv, MR were averaged. The Surface roughness Ra measured by tester SRT-6210- surface roughness, Time tester was used to measuring Hv, the MR measured through measuring the weight of the workpiece before and after process (∆w) using the delicate balance. For steel material, 27 tests were applied. The last step adjusts the value of the six input parameters according to Taguchi matrix, and fixed the workpiece on the table of the milling machine, then filled the working gap with powder (5cm3). Table 1, Input parameters values. Input Levels Symbol Level 1 Level 2 Level 3 Amplitude of pole geometry (mm) X1 4 8 12 Number of cycles of pole geometry X2 2 5 8 Finishing time (min) X3 5 10 15 Cutting velocity (rpm) X4 175 580 970 Current (Amp) X5 1.0 1.5 2.0 Working gap (mm) X6 1.0 2.0 3.0 3. Results and Discussion The criterion outputs, ∆Ra, ∆Hv, and MR are dependent variable in regression models, while the predictor’s factors were the amplitude of pole geometry, number of cycles of pole geometry, finishing time, and cutting velocity, current and working gap. Table (2) shows the result of experiment for ferromagnetic material. Saad kariem Shather Al-Khwarizmi Engineering Journal, Vol. 11, No. 4, P.P. 82- 88(2015) 84 Table 2, Results of experiments for steel 304 and distribution parameters according to Taguchi matrix L27. № X1 X2 X3 X4 X5 X6 ∆Ra, µm ∆w MR ∆Hv 1 1 4 1 2 1 5 1 175 1 1 1 1 0.292 0.001 10.5 2 1 4 1 2 1 5 1 175 2 1.5 2 2 0.278 0.0012 9 3 1 4 1 2 1 5 1 175 3 2 3 3 0.383 0.3036 8.6 4 1 4 2 5 2 10 2 580 1 1 1 1 0.387 0.0024 6.6 5 1 4 2 5 2 10 2 580 2 1.5 2 2 0.466 0.0016 10.8 6 1 4 2 5 2 10 2 580 3 2 2 2 0.233 0.0041 7.5 7 1 4 3 8 3 15 3 970 1 1 1 1 0.352 0.0026 11.5 8 1 4 3 8 3 15 3 970 2 1.5 2 2 0.377 0.002 8.7 9 1 4 3 8 3 15 3 970 3 2 3 3 0.044 0.0068 8 10 2 8 1 2 2 10 3 970 1 1 2 2 0.099 0.0098 25 11 2 8 1 2 2 10 3 970 2 1.5 3 3 0.108 0.0044 11.6 12 2 8 1 2 2 10 3 970 3 2 1 1 0.064 0.0084 9.6 13 2 8 2 5 3 15 1 175 1 1 2 2 0.118 0.0011 21.1 14 2 8 2 5 3 15 1 175 2 1.5 3 3 0.21 0.0051 22.8 15 2 8 2 5 3 15 1 175 3 2 1 1 0.672 0.0094 13.5 16 2 8 3 8 1 5 2 580 1 1 2 2 0.241 0.0013 20.7 17 2 8 3 8 1 5 2 580 2 1.5 3 3 0.225 0.0014 3.8 18 2 8 3 8 1 5 2 580 3 2 1 1 0.378 0.0063 7.7 19 3 12 1 2 3 15 2 580 1 1 3 3 0.028 0.002 15.5 20 3 12 1 2 3 15 2 580 2 1.5 1 1 0.164 0.0425 17.4 21 3 12 1 2 3 15 2 580 3 2 2 2 0.111 0.0023 26.7 22 3 12 2 5 1 5 3 970 1 1 3 3 0.102 0.0079 25.9 23 3 12 2 5 1 5 3 970 2 1.5 1 1 0.068 0.0027 8.3 24 3 12 2 5 1 5 3 970 3 2 2 2 0.038 0.0023 5.8 25 3 12 3 8 2 10 1 175 1 1 3 3 0.14 0.0017 16.8 26 3 12 3 8 2 10 1 175 2 1.5 1 1 0.393 0.0099 11.8 27 3 12 3 8 2 10 1 175 3 2 2 2 0.287 0.0055 21.7 3.1. Regression Model for Surface Roughness (Ra for Steel 304) Versus x1; x2; x3; x4; x5; x6 By using Minitab 16 statistical software, finding the mathematical statistical regression models for MAF process between the surface roughness and all six parameters are represented bellow. Ra st. = 0.494 - 0.0196 x1 + 0.0169 x2 + 0.00079 x3 - 0.000212 x4 + 0.0423 x5 - 0.0707 x6 …(1) The regression analysis of variance (ANOVA) on to surface finish ∆Ra for steel 304. The results of analysis were show in Table (3). Table 3, Result of ANOVA. Predictor Coefficient P Effect inductor X1 -0.019587 0.008 significant effect (p<0.05) X2 0.016852 0.072 mildly significant effect (p<0.1) X3 0.000789 0.884 insignificant effect (p> 0.1) X4 -0.000213 0.005 significant effect (p<0.05) X5 0.04225 0.438 insignificant effect (p> 0.1) X6 0.07073 0.018 significant effect (p<0.05) Analysis of Variance for regression also show: R-Sq = 60.5% F =5.11 P =0.003 The R-sq showed that 60.5% of the observed variable in surface roughness for steel was independent variable. F- Value was high; P-value for regression equation was significant effect. The coefficients (of output parameters) for regression are listed in the Table (3). For these coefficients, multiple linear regressions (mathematical statistical model) for surface roughness with steel materials could be expressed equation (1). Saad kariem Shather Al-Khwarizmi Engineering Journal, Vol. 11, No. 4, P.P. 82- 88(2015) 85 3.1.1. The Effects of Amplitude, Velocity of Pole, and Working Gap on the Surface Roughness ∆Ra for Steel 304. However for the six input factors, all coefficient of the linear regression equation 1, analysis of variance and instruction curves figure 2 indicate some significant. X1; X4; X6 have a significant effect on the surface roughness ∆Ra st., curves shows that if the X1; X4; X6 increases, the surface roughness ∆Ra for steel decreases. The influence of amplitude (X1) that has a significant effect on surface roughness as follow: the increases in amplitude from 4 to 12 mm lead to decreases in the ∆Ra from 0.3 to 0.15 µ m improved to 30%. From all six parameters. This figure also shows that an increases in cutting velocity X4 from 175 to 970 rpm lead to reduce in the ∆Ra st. from 0.3 to 0. 15 µ m. In the same way decrease in working gap X6 from 1 to 3 mm lead to reduce in the ∆Ra st. from 0.3 to 0.15 µ m improved the surface roughness to 32%. Working gap X6 improving the surface roughness to 24%, 3.1.2. The Effect of Number of Cycles on the Surface Roughness ∆Ra . The number of cycles X2 has mildly significant effect on the ∆Ra, compared with amplitude, cutting velocity and working gap. Figure 2 shows if the number of cycles increases from 2 to 9 the ∆Ra st. increases from 0.15 to 0.3 µ m that means improved in the surface roughness. X2 improve surface roughness to 11%, current improved the surface roughness to 3%, while X3 finishing time insignificant. Fig. 2. Main effects of process parameters on the surface roughness ∆Ra st. 3.2. Regression model for material removal MR (steel 304) versus x1; x2; x3; x4; x5; x6 By using Minitab 16 statistical software, finding the mathematical statistical regression models for MAF process between removal rate and all six parameters are represented in equation 2. MR st. = 0.0387 - 0.00370 x1 - 0.00625 x2 - 0.00282 x3 - 0.000041 x4 + 0.0374 x5 + 0.0175 x6 …(2) The regression analysis of variance (ANOVA) on to removal rate for steel 304 The results of analysis were show in Table (4). Table 4, Result of ANOVA. Predictor Coefficient P X1 -0.003695 0.259 X2 -0.006254 0.156 X3 -0.002821 0.280 X4 -0.000041 0.215 X5 0.03738 0.158 X6 0.01754 0.197 Analysis of Variance for regression also show: R-Sq = 33.2% F-value = 1.66 P = 0.18 This regression has insignificant effect because (p> 0.1) and R-sq denotes an observation with a large standardized residual, F-value was low. See Figure 3. Fig. 3. Main effects of process parameters on the MR st. Saad kariem Shather Al-Khwarizmi Engineering Journal, Vol. 11, No. 4, P.P. 82- 88(2015) 86 3.3. Regression Model for Micro-hardness Hv (steel 304) versus x1; x2; x3; x4; x5; x6 . By using Minitab 16 statistical software, finding the mathematical statistical regression models for MAF process between the micro- hardness Hv and all six parameters are represented bellow. HV st. = 9.46 + 0.935 x1 - 0.430 x2 + 0.499 x3 - 0.00300 x4 - 4.79 x5 +1.39 x6 …(3) The regression analysis of variance (ANOVA) on to micro-hardness Hv for steel 304. The results of analysis were show in Table (5). Table 5, Result of ANOVA. Predictor Coefficient P Effect inductor X1 0.9349 0.010 significant effect (p<0.05) X2 -0.4296 0.337 insignificant effect (p> 0.1) X3 0.4989 0.072 mildly significant effect (p<0.1) X4 -0.003003 0.373 insignificant effect (p> 0.1) X5 -4.790 0.083 mildly significant effect (p<0.1) X6 1.388 0.318 insignificant effect (p> 0.1) Analysis of Variance for regression also shows: R-Sq = 50.1% F = 3.08 P = 0.026 significant effect in the process MAF respect to micro- hardness. The R-sq showed that 50.1% of the observed variable in micro-hardness for steel was independent variable. F- Value was high; P-value for regression equation was significant effect. The coefficients (of output parameters) for regression are listed in the column above. For these coefficients multiple linear regressions (mathematical statistical model) for surface roughness with steel materials could be expressed, see equation 3. 3.3.1. The Effects of Amplitude on the Micro-Hardness ∆Hv st The effect of amplitude X1 has a significant effect on the ∆Hv st. compared with other parameters, figure 4 show if the amplitude increases from 4 to 12 the ∆Hv st. increases from 8 to 17 Mpa, and improved in the surface quality about 42%. 3.3.2. The Effects of Finishing Time and Current on the Micro-Hardness ∆Hv st The effect of finishing time X3 and current X5 have a mildly significant effect on the micro- hardness Hv compared with amplitude, figure 4 shows, if the finishing time increases from 5 to 15 the ∆Hv st. increases from 8 to 17 Mpa, and improving in the surface quality about 20%. If the current increases from1to 2 Amp the micro- hardness ∆Hv decreases from 17 to 12 Mpa and improved the quality to 18%. 3.3.3. The Effects of Number of Cycle, Cutting Velocity and Working Gap on the Micro-Hardness ∆Hv st The effect of number of cycle X2 and cutting velocity X4 have insignificant effect on the micro- hardness because the p-value was p> 0. 1. Improving the quality about 5%. Fig. 4. Main effects of process parameters on the micro-hardness ∆Hv for steel. Saad kariem Shather Al-Khwarizmi Engineering Journal, Vol. 11, No. 4, P.P. 82- 88(2015) 87 4. Conclusions This study shows the influence of design and technological parameters, amplitude of pole geometry, number of cycles of pole geometry, finishing time, cutting velocity, Current and working gap on the MAF output process. Generate regression models for surface roughness, micro-hardness and material removal, and by using regression analysis of variance (ANOVA), the influence on the MAF process as follow: The parameter X1, (the amplitude of pole geometry) has significant effect on the surface roughness Ra, which improved the surface roughness about 30%. , This parameter X1 has significant effect on the micro-hardness about 42%. While this parameter X1 has insignificant effect on the MRR. The effect of another parameter (X2, X3, X4, X5, and X6) on the properties of the surface layer (roughness, micro-hardness, and removal rate). Were puts in Table 6. Table 6, Conclusions. Parameters Influence on ∆Ra Improving ∆Ra% Influence on ∆Hv Improving ∆Hv% Influence on MR X1 Significant 30 Significant 42 insignificant X2 Mild significant 11 insignificant 6 insignificant X3 Insignificant 0.0 Mild significant 20 insignificant X4 Significant 32 In significant 5 insignificant X5 Insignificant 3 Mild significant 18 insignificant X6 Significant 24 Insignificant 9 insignificant Total 100% Total 100% 5. References [1] H. Kumar, S. Singh, and P. Kumar, “Magnetic Abrasive Finishing- A review”, International Journal of Engineering Research & Technology (IJERT), Vo. l, Issue 3, March 2013. [2] Jain VK, “ Advanced Machining Processes”. Allied publishers, Delhi, 2002. [3] Yamaguchi H, and sato T, “ Polishing and Magnetic Field- Assisted Finishing”, Intelligent Energy Field Manufacturing Interdisciplinary Process Innovations, 2012. [4] Yu. M. Baron, “ Technology of Abrasive Machining in a Magnetic Field”, Mashinostroenie, Leningrad 1975. [5] N. K. Jain, V.K. Jain, and S. Jha, “Parametric Optimization of Advanced Fine Finishing Processes”, International Journal of Advanced manufacturing Technology, Vol. 34, Issue 11-12, pp 1191-1213, November 2007. [6] Yin. S. and Shinmura T, “ Study of Magnetic Field- Assisted Machining Process for Ferromagnetic Metallic Materials”, Journal of Japan Soc. Abrasive Technology, Vol. 46, Issue 3, pp. 141- 145, 2002. )2015( 82- 88، ;:9/ 4، ا-'&د11&+*/ ا-56ارز(3 ا-12&)0/ ا-,+*()'& %$#" ! ر 88 �ت ا �� �ط�������ر ا �وا�ل ا ������� وا �� و و��� ��� �� ����� ا ��ط�ب ��� ��ر� �� *��� &%$#د �!�!�** ا-+=('/ ا-@DE //0F5-51G" ھ1&)/ اAB@=ج وا-,'=دن* ا-,I0D - ا-,'2& ا-@DE /31H" اAB@=ج** *3Aو$@G-Bا &#$J-ا :drengsaad_k_sh@yahoo.com **3Aو$@G-Bا &#$J-ا: shakir.aljabiri_89@yahoo.com ا+*()' 3D01=طL,-ا M91-=N I0OP@-0/ -',*0/ ا*,Q رب=+S 0:1S "S )MAF (و T*Q UV0,0/ا,W@-0$ات اL@,- )/'D-ا، IOH-ا YG! 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