In this study, the eff ects of cutting parameters on kerf and surface roughness were experimentally investigated in WEDM. Al 7 475-T7 351 alloy was selected as the work material to conduct experiments. The factors selected for the optimization are the pulse on time, table feed rate and the wire speed, each of the factors in three diff erent levels. An optimal parameter combination of the WEDM process was obtained by applying the grey relational analysis (GRA). Also, the analysis of variance (ANOVA) was carried out for fi nding out the contribution and the eff ects of machining parameters on the multiple performance characteristics (MPC)

U. Köklü

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47 METALURGIJA 51 (2012) 1, 47-50U. KÖKLÜOPTIMIZATION OF KERF AND SURFACE ROUGHNESS OF AL 7 475-T7 351 ALLOY MACHINED WITH WEDM PROCESS USING THE GREY-BASED TAGUCHI METHODReceived – Prispjelo: 2010-12-22Accepted – Prihvaćeno: 2011-03-03Original scientifi  c Paper – Izvorni znanstveni radISSN 0543-5846METABK 51(1) 47-50 (2012)UDC – UDK 621.96:621.795=111In this study, the eff  ects of cutting parameters on kerf and surface roughness were experimentally investigated in WEDM. Al 7 475-T7 351 alloy was selected as the work material to conduct experiments. The factors selected for the optimization are the pulse on time, table feed rate and the wire speed, each of the factors in three diff  erent levels. An optimal parameter combination of the WEDM process was obtained by applying the grey relational analysis (GRA). Also, the analysis of variance (ANOVA) was carried out for fi  nding out the contribution and the eff  ects of ma-chining parameters on the multiple performance characteristics (MPC). Key words: WEDM (Wire Electrical Discharging Machining), Al 7 475-T7 351, Grey relational analysis.Optimiranje kvalitete reza i hrapavosti površine Al 7 476-T7 351 legure obrađene WEDM postupkom uz primjenu “gray-based / sivo temeljene” Taguči metode. U ovom radu se obrazlažu rezultati eksperimentalnog istraživanja utjecaja parametara rezanja na kvalitetu reza i hrapavosti površine Al 7 476-T7 351 legure. Izabrani su faktori za optimiranje: vremenski impulse, brzina dodavanja ploče i brzina žice. Svaki od odabranih parametara iza-bran je na tri različita nivoa. Optimalna kombinacija parametara rezanja je dobivena primjenom “grey-sive” relacijske analize (GRA). Također je provedena i analiza varijance (ANOVA) za pronalaženje pojedinačnih doprinosa i efekata pojedinih parametara obrade.Ključne riječi: WEDM, Al 7 476-T7 351, “grey-siva” relacijska analizaINTRODUCTIONOwing to low densities and good mechanical prop-erties, 2xxx and 7xxx series aluminum alloys have been broadly used as structural materials in aeronautical in-dustry. Among these alloys, 7 475 series alloys are suc-cessfully used thanks to superior fracture toughness [1]. Nevertheless, those materials are difﬁ  cult to be ma-chined by traditional cutting methods. Hence, non-tra-ditional materials removal process including electrolytic grinding, supersonic machining and electrical discharg-ing machining are applied [2]. Recently, WEDM has become an important non-traditional materials removal process, extensively used in the aerospace and automo-tive industry [3]. Many studies have been done interested in kerf and surface roughness in WEDM operations. The machina-bility of standard GGG40 nodular cast iron by WEDM using different parameters was investigated by Ozdemir and Ozek [4]. They used a regression model to estimate cutting rate and surface roughness using machining pa-rameters. Gökler and Ozanözgü [5] experimentally in-vestigated the effect of cutting and offset parameter U. Köklü, Technical Education Faculty, Dumlupinar University, Simav-Kutahya, Turkeycombinations on the surface roughness. They concluded that the surface roughness of the process is closely de-pendent on machining parameters. Tosun et al. [6,7] op-timized machining parameters on the cutting width, sur-face roughness and material removal rate in WEDM operations and they derived a mathematical model. Ra-makrishnan and Karunamoorthy [8] developed artiﬁ  cial neural network model and MPC using multi response signal-to-noise ratio, to achieve better material removal rate and surface ﬁ  nish simultaneously.Muthu Kumar et al. [9], Mahapatra and Patnaik [10] and Shah et al. [11] investigated experimentally the in-ﬂ  uence of the machining parameter on the kerf width, metal removal rate and the surface roughness of the ma-chined workpiece surface using Taguchi method. Above mentioned and other experimental investigations have shown that the kerf and surface roughness of machined workpiece is strongly dependent on cutting parameters. In this study, grey-based Taguchi method for mini-mizing both the kerf and the surface roughness were used. Experiments were performed under different cut-ting condition, such as pulse on time, table feed rate and wire speed. The kerf and surface roughness of the ma-chined workpiece are measured and analyzed through Taguchi and GRA to determine the optimal parameters in WEDM process. 48   METALURGIJA 51 (2012) 1, 47-50U. KÖKLÜ: OPTIMIZATION OF KERF AND SURFACE ROUGHNESS OF AL 7 475-T7 351 ALLOY MACHINED WITH WEDM...GREY ANALYSISIn GRA the ﬁ  rst step is to perform data pre-process-ing. In particular, to avoids the problem of different scales, units and targets. Experimental results are nor-malized in the range between zero and one. Then, the second step is to calculate the grey relational coefﬁ  cient from the normalized experimental results to express the relationship between the best and the actual experimen-tal results [12]. Kerf and surface roughness which are lower-the-better performance characteristic are pre-processed as follows: ) ( min ) ( max) ( ) ( max) ( 0 00 0*k x k xk x k xk xi ii ii −−=    (1)where:  ) (* k xi  is the value after grey relational gen-eration,  ) (0 k xi  the original value, min  ) (0 k xi  the smallest value of  ) (0 k xi  and max  ) (0 k xi  the largest value of ) (0 k xi . The higher preprocessed value shows better per-formance and best preprocessed result should be equal to one. The grey relational coefﬁ  cient [ξi(k)] can be ex-pressed as follows:  max 0max min) () (∆ + ∆∆ + ∆=kkii (2)Where ζ is the distinguishing coefﬁ  cient (∈[0–1]), ∆0i(k), the difference in absolute value between  ) (* k x0   and  ) (* k xi ,  ) (* k x0  the ideal or reference sequence, ∆min the smallest value of ∆0i and ∆max the largest value of  ∆0i. Grey relational grade is computed by averaging the grey relational coefﬁ  cients corresponding to each per-formance characteristics. The experimental result of the kerf and surface roughness are evaluated by using this grey relational grade. The optimum level of the process parameters is the level with the highest grey relational grade. The grey relational grade can be calculated as:  ∑==nki i kn 1) (1    (3)where γi is the grey relational grade and n is the number of performance characteristics.EXPERIMENTAL WORKIn this study, Al 7 475-T7 351 was chosen as the work materials because of its extensive use in the aero-space industry. The mechanical properties and chemical compositions of Al alloy were presented in Table 1. The machining experiments were performed on ﬁ  ve-axes CNC WEDM. In the experiment, coated brass wire (CuZn 37) of 0,25 mm diameter was used as electrode and the ion water is used as the dielectric liquid. During all experiments, tension of the wire of 800 g, upper noz-zle set 25 mm, cutting length of 20 mm and workpiece thickness of 12,7 mm were kept constant. The experi-ments were carried out according to Taguchi’s L27 or-thogonal array design matrix. The experiments are de-signed at three levels as shown in Table 2. Machine table feed rate is controlled step by step. By raising step posi-tion from 4 to 6 the table feed rate (mm/min) is reduce, in other words it is changed inversely proportional. In this study, kerf and surface roughness were se-lected as the quality characteristics for optimizing ma-chining parameters. The kerf (K) and surface roughness (Ra, the arithmetic average) of an each machined work-piece was measured by means of an optical microscope and surface roughness tester (Mahr Perthometer) using a meter cut off 1,750 mm respectively. Kerf and surface roughness measurements are shown in Figure 1.Table 2   Control factors and their levels used for experiment# Factors UnitLevel1231 Pulse on time (A) µs 0,2 0,4 0,62 Table feed rate (B) step 4 5 63 Wire speed (C) m/min 1 2 3RESULTS AND DISCUSSIONThe optimum parameters settings and the most inﬂ  u-ential factor of the kerf and surface roughness are deter-mined by both the grey relational grade and the analysis of variance. In the present study, the measured data (K and Ra), grey relational coefﬁ  cients, grey relational coefﬁ  cients after weighed, grey relational grade and rank evaluated for each group are shown in Table 3. Typically, smaller the better quality characteristic for both the kerf and the surface roughness should be taken for obtaining optimal machining performance. Grey relational grade can be calculated by using equation (1-3). Always the highest grey relational grade is preferred. WEDM parameter setting of experiment number 7 has the highest grey relational grade clearly Table 1 Mechanical and chemical properties of Al 7 475-T7 351 aluminum alloyMaterialsTensile properties Chemical compositionsRm /MPa Re /MPa Elongation /% Al Cr Cu Fe Mg Mn Si Ti ZnAl 7 475-T7 351 490 414 9 Base 0,20 1,5 0,12 2,0 0,06 0,10 0,06 5,3Figure 1 Measurement of kerf and surface roughness49 METALURGIJA 51 (2012) 1, 47-50U. KÖKLÜ: OPTIMIZATION OF KERF AND SURFACE ROUGHNESS OF AL 7 475-T7 351 ALLOY MACHINED WITH WEDM...shown in Table 3 and Figure 2. Thus, the seventh ex-periment gives the best MPC simultaneously among the 27 experiments. The machining parameters in experi-ment number 7 are: (A) Pulse on time 0,2 µs; (B) table feed rate 6 step and (C) wire speed 1 m/min. The response graph for the overall grey relational grade is presented graphically in Figure 3. It shows that, the larger the grey relational grade, the better is the MPC. The optimized parameters are A1B3C1. The kerf and surface roughness diminishes with increasing table feed rate (as step), decreasing the pulse on time and wire speed (Figure 3).The standard commercial statistical software package Minitab 15,0 was used in the analysis of results. Table 4 shows the result of ANOVA analysis and indicates that pulse on time, table feed rate and wire speed are signiﬁ  -cant (at the 95 % conﬁ  dence level) parameters which in-ﬂ   uence the kerf and surface roughness. Furthermore, pulse on time has the most signiﬁ  cant impact on the MPC due to its highest percentage contribution among the process parameters. From Table 4, it can be seen that the pulse on time (74,505 %), table feed rate (6,307 %) and wire speed (6,054 %) have statistical and physical sig-niﬁ  cance on the kerf and surface roughness.In this study, a veriﬁ  cation experiment was carried out by utilizing the experimental condition of the three factors as: Pulse-on time 0,2 µs, table feed rate 6 step and wire speed 1 m/min (A1B3C1) for the minimum kerf and surface roughness obtained through grey relation process. Table 5 shows the comparisons of predicted Table 3 The measured data, the evaluated grey relational co-effi   cients, grades and rankGroup no Measured data Grey relational coeffi   cientsGrey relational coeffi   cients after weighedGrey relational gradeKerf Ra Kerf Ra Kerf Ra Grade S/N Rank1 0,29 2,816 0,90909 0,72414 0,84615 0,64444 0,74530 -2,55431 42 0,31 2,922 0,72727 0,58355 0,64706 0,54559 0,59632 -4,49114 73 0,32 2,943 0,63636 0,55570 0,57895 0,52949 0,55422 -5,12667 84 0,29 2,753 0,90909 0,80769 0,84615 0,72222 0,78419 -2,11290 35 0,30 2,843 0,81818 0,68833 0,73333 0,61601 0,67467 -3,41882 66 0,31 2,922 0,72727 0,58355 0,64706 0,54559 0,59632 -4,49114 77 0,28 2,608 1,00000 1,00000 1,00000 1,00000 1,00000 0,00000 18 0,28 2,683 1,00000 0,90053 1,00000 0,83407 0,91704 -0,75252 29 0,29 2,822 0,90909 0,71618 0,84615 0,63790 0,74203 -2,59274 510 0,34 3,105 0,45455 0,34085 0,47826 0,43135 0,45481 -6,84378 1411 0,32 3,089 0,63636 0,36207 0,57895 0,43939 0,50917 -5,86343 912 0,35 3,098 0,36364 0,35013 0,44000 0,43483 0,43742 -7,18242 1613 0,34 3,074 0,45455 0,38196 0,47826 0,44721 0,46274 -6,69382 1114 0,35 3,122 0,36364 0,31830 0,44000 0,42312 0,43156 -7,29938 1715 0,37 3,031 0,18182 0,43899 0,37931 0,47125 0,42528 -7,42711 2016 0,33 3,003 0,54545 0,47613 0,52381 0,48834 0,50608 -5,91613 1017 0,35 3,029 0,36364 0,44164 0,44000 0,47243 0,45622 -6,81689 1318 0,36 3,020 0,27273 0,45358 0,40741 0,47782 0,44261 -7,07997 1519 0,38 3,174 0,09091 0,24934 0,35484 0,39979 0,37731 -8,46626 2320 0,39 3,254 0,00000 0,14324 0,33333 0,36852 0,35093 -9,09584 2521 0,39 3,362 0,00000 0,00000 0,33333 0,33333 0,33333 -9,54251 2622 0,35 3,008 0,36364 0,46950 0,44000 0,48520 0,46260 -6,69645 1223 0,36 3,064 0,27273 0,39523 0,40741 0,45258 0,42999 -7,33103 1824 0,37 3,284 0,18182 0,10345 0,37931 0,35802 0,36867 -8,66748 2425 0,35 3,131 0,36364 0,30637 0,44000 0,41889 0,42944 -7,34235 1926 0,36 3,214 0,27273 0,19629 0,40741 0,38352 0,39546 -8,05839 2127 0,37 3,155 0,18182 0,27454 0,37931 0,40801 0,39366 -8,09779 22Figure 2   Variation of grey relational grade with experiment number Figure 3 Eff  ect of WEDM parameter levels on the K and Ra50   METALURGIJA 51 (2012) 1, 47-50U. KÖKLÜ: OPTIMIZATION OF KERF AND SURFACE ROUGHNESS OF AL 7 475-T7 351 ALLOY MACHINED WITH WEDM...and actual machining performance for MPC using their optimal WEDM parameters. The value of kerf and sur-face roughness at this optimum level has been found as 0,28 and 2,620, respectively, after conformation run (Table 5). As shown from table, kerf decrease from 0,35 mm to 0,28 mm; and surface roughness improve from 3,122 µm to 2,620 µm. It obviously demonstrates that the kerf and surface roughness are greatly improved through this approach.CONCLUSIONSIn order to minimize both the kerf and the surface roughness of Al 7 475-T7 351, effect of various cutting parameters have been investigated in WEDM grey-based Taguchi method. The following conclusions can be drawn from the investigation:The increase of the pulse on time, wire speed and decrease of table feed rate increase both the kerf and the surface roughness. Through the grey-Taguchi method, the decrease in the kerf and surface roughness of Al 7 475-T7 351 from the initial machining setting (A2B2C2) to the optimized condition (A1B3C1) is 0,07 mm and 0,502 µm, respec-tively. The optimum condition of parameters setting is as pulse on time 0,2 µs, table feed rate 6 step and wire speed 1 m/min for minimizing both the kerf and the sur-face roughness. The results of ANOVA (at a 95 % conﬁ  dence level) for the machining indicated that the pulse on time, table feed rate and wire speed have inﬂ  uence on the both the kerf and the surface roughness by 74,505 %, 6,307 % and 6,054 % respectively.REFERENCES[1]  K.S. Al-Rubaie, E.K.L. Barroso, L.B. Godefroid, Interna-tional Journal of Fatigue 28 (2006), 934–942.[2]  Y.S. Liao, J.T. Huang, Y.H. Chen, Journal of Materials Pro-cessing Technology 149 (2004), 165–171.[3]  M.S. Hewidy, T.A. El-Taweel, M.F. El-Safty, Journal of Materials Processing Technology 169 (2005), 328–336.[4]  N. Ozdemir, C. Ozek, Int. J. Adv. Manuf. Technol. 28 (2006), 869-872.[5]  M.I. Gökler, A.M. Ozanözgü, International Journal of Ma-chine Tools & Manufacture 40 (2000), 1831–1848.[6]  N. Tosun, C. Cogun, A. Inan, Machining Science And Te-chnology 7 (2003), 209–219.[7]  N. Tosun, C. Cogun, G. Tosun, Journal of Materials Pro-cessing Technology 152 (2004), 316–322. [8]  R. Ramakrishnan, L. Karunamoorthy, Journal of Materials Processing Technology 207 (2008), 343–349.[9]  V. Muthu Kumar, A. Suresh Babu, R. Venkatasamy, M. Raajenthiren, International Journal of Engineering Science and Technology 2 (2010), 1538-1547.[10]  S.S. Mahapatra, A. Patnaik, Int. J. Adv. Manuf. Technol. 34 (2007), 911–925.[11]  A. Shah, N.A. Mufti, D. Rakwal, E. Bamberg, Journal of Materials Engineering and Performance, DOI: 10.1007/s11665-010-9644-y.[12]  B.M. Gopalsamy, B. Mondal, S. Ghosh, Int. J. Adv. Ma-nuf. Technol. 45 (2009), 1068–1086.Note:   The responsible translator for the English language is the lecturer from Technical Education Faculty, Simav-Kutahya, TurkeyTable 4 Results of ANOVA SD fS S V F %A 2 0,5898   0,2949   56,73   74,505B  2 0,0499   0,0249   4,80   6,307C 2 0,0479   0,0239   4,61   6,054Error 20 0,1039   0,0052 - 13,133Total 26 0,7916 - - 100S: Symbol, Df: Degree of freedom, SS: Sum of squares, V: Variance, F: F ratio, %: Percent.Table 5 Results of the verifi  ed experiment for K and RaInitial parametersOptimal WEDM parametersPrediction ExperimentalSetting level A2B2C2 A1B3C1 A1B3C1Kerf (mm) 0,35 - 0,28Surface roughness (µm) 3,122 - 2,620Grey relational grade 0,43156 1,0017 0,98458Improvement of the grey reletional grade= 0,55302

Optimization of kerf and surface roughness of Al 7 475-T7 351 alloy machined with WEDM process using the grey-based Taguchi method

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Optimization of kerf and surface roughness of Al 7 475-T7 351 alloy machined with WEDM process using the grey-based Taguchi method

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