Design and Manufacturing Engineering Department, Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia 2

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1 OPTIMIZATION OF MULTIPLE PERFORMANCE CHARACTERISTICS IN WIRE ELECTRICAL DISCHARGE MACHINING (WEDM) PROCESS OF BUDERUS 28 TOOL STEEL USING TAGUCHI-GREY- FUZZY METHOD D. A. Purnomo 1,2, B. O. P. Soepangkat 2 and H. C. Ks Agustn 3 1 Desgn and Manufacturng Engneerng Department, Polteknk Perkapalan Neger Surabaya, Surabaya, Indonesa 2 Manufacturng Process Lab., Mechancal Engneerng Department, Insttut Teknolog Sepuluh Nopember, Surabaya, Indonesa 3 Metallurgcal Lab., Mechancal. Engneerng Department, Insttut Teknolog Sepuluh Nopember, Surabaya, Indonesa E-Mal: dc_adtya@yahoo.com ABSTRACT Ths paper presents an optmzaton of machnng parameters on the materal removal rate (MRR), cuttng wdth (kerf), surface roughness (SR) and recast layer thckness (RL) of WEDM process. Buderus 28 tool steel was selected as workpece materal. The combnatons of machnng parameters were determned by usng Taguch expermental desgn method. The four mportant machnng parameters such as arc on tme, on tme, open voltage and servo voltage were taken as process varables. Optmal machnng parameter were obtaned by grey relatonal analyss and fuzzy logc method. Expermental results show that on tme gves the hghest contrbuton for reducng the total varaton of the multple responses, followed by open voltage, servo voltage and arc on tme. The maxmum materal removal rate and mnmum cuttng wdth, surface roughness and recast layer thckness could be obtaned by usng the values of arc on tme, on tme, open voltage and servo voltage of 1 A, 2 µs, 75 V and 3 V respectvely. Keywords: WEDM, buderus 28, taguch method, grey relatonal analyss, fuzzy logc. INTRODUCTION WEDM process s wdely used n machnng of complex components made of hgh hardness, hgh toughness and conductve materals. Cuttng process n WEDM s caused by thermal energy of dscrete sparks between the workpece and the electrode. The sparks erode workpece and small part of wre electrode. The eroded materals are flushed away from the machnng zone by the delectrc flud [1]. Materal removal rate (MRR), cuttng wdth (kerf), surface roughness (SR) and recast layer thckness (RL) are several responses that used to evaluate the performances of WEDM process. Cuttng speed or MRR affects the producton rate. However, the maxmum MRR s lmted by the wre rupture possblty. Kerf determne the degree of accuracy of workpece dmensons [2]. SR s also an aspect requrng specal attenton because of ts effect to the surface qualty. The thck RL formed durng WEDM process reduces the surface mechancal propertes and ncreases the surface roughness. Optmzng multple performance characterstcs at the same tme n the WEDM process needs proper machnng parameters settng. Based on the revew lteratures [3-7] and prelmnary research, the most mportant machnng parameters of WEDM process are on tme, open voltage, servo voltage and arc on tme. Hence, those machnng parameters need to be selected properly n terms of the machnng tool, materal propertes and wre electrode n order to maxmze MRR and mnmze SR, kerf and RL smultaneously. The grey relatonal analyss method was developed by Deng [8]. Ths method provdes technques for determnng a good soluton for the unknown nformaton. The grey relatonal analyss can fnd out the relaton between machnng parameters and machnng performances. The term of fuzzy logc was ntroduced by Zadech [9]. Taguch method only focused on optmzng sngle performance characterstc [3]. However, product n some machnng processes have more than one machnng performance whch should be consdered. By usng fuzzy logc multple objectve optmzaton problem can be solved by transformng multple qualty characterstcs nto sngle qualty characterstc. In fact, there are three defntons of performance characterstcs, namely lowers-better, hgher-s-better, and nomnal-s-better. The man purpose of ths research s to dentfy the combnaton of the machnng parameters for achevng requred multple performance characterstcs n WEDM process usng grey relatonal analyss, fuzzy logc and orthogonal array based on the Taguch method. EXPERIMENTAL DESIGN Equpments and Materal In ths study, a fve axs CNC Wre-cut EDM (CHMER CW32F) was used as the expermental machne. A.25 mm dameter of AC CUT VS 9 znc coated brass wre used n ths expermental as an electrode to erode a work pece of Buderus 28 tool steel plate wth hardness of 62 HRC. A typcal composton of 2779

2 Buderus 28 tool steel conssts of 2.1% C,.3% S,.3% Mn and 12.% Cr. Durng the experments 1 mm length of cut was performed on the workpece wth 2 mm n length, 3 mm n wdth and 15 mm n thckness. MRR s defned as volume of removed materal per unt tme [2]. Measurements of the SR were taken by usng a Mtutoyo Surftest 31 wth samplng length of.8 mm. Scannng electron mcroscope (SEM) was used to measures the RL thckness and the kerf was measured by usng Nkon measurescope 2. Based on the revew lterature and prelmnary nvestgaton, the machnng parameters were selected and shown n Table-1. Table-1. Machnng parameters. Machnng parameters Arc on tme [AN, ampere] On tme [ON, µs] Open voltage [OV, volt] Servo voltage [SV, volt] Desgn of Experment Taguch method wth an orthogonal array was used to desgn the experment. The total degrees of freedom (DOF) need to be calculated to select a proper orthogonal array. Ths experment has seven DOF because three machnng parameters havng three levels and one machnng parameters havng two levels as shown n Table-1. The DOF for the orthogonal array should be equal to or greater than DOF of the machnng parameters [11]. Therefore, a mxed L 18 (2 1 x3 3 ) orthogonal array was chosen for the desgn of experment and t s shown n Table-2. To reduce nose factors a random order was appled for runnng the tests. S. No Table-2. A mxed L 18 (2 1 x3 3 ) orthogonal array. AN [ampere] Machnng parameter ON [µs] OV [volt] SV [volt] Optmzaton of Multple Performance Characterstc wth Taguch-Grey-Fuzzy Method The mult objectve optmzaton problem can be solved wth Taguch-grey-fuzzy method. Fgure-1 shows the calculaton sequence of Taguch grey fuzzy method. Expermental Result and Analyss The expermental results and sgnal-to-nose ratos (S/N ratos) of MRR, kerf, SR and RL are shown n Tabel-3. The S/N rato s an effectve value to fnd out the sgnfcant machnng parameters by evaluatng mnmum varance. S/N rato s used to measure the qualty characterstc devatng from the targeted value [8]. There are three dfferent types of the qualty performance characterstcs to calculate the S/N rato,.e., hgher-sbetter, lower-s-better and nomnal-s-better. The better machnng performance can be obtaned through the maxmum MRR and the mnmum kerf, SR and RL. The determnaton of whch equaton to be employed for calculatng S/N rato s based on the types of the qualty performance characterstc of each response. In ths experment the qualty performance characterstc of MRR s hgher-s-better and the qualty performance characterstcs of kerf, SR and RL are lower-s-better. The S/N rato for each performance characterstc can be expressed by the followng equatons [8]: 278

3 Lower-s-better: n 2 y S/N = -1 log, and (1) 1 n Hgher-s-better: n (1/ y S/N = -1 2 ) log (2) 1 n where n represent the number of the test, and y s the measured value. However, whatever the type of the performance characterstcs s, the greater value of S/N rato ndcates the better machnng performance. Table-3. Expermental result of all responses and the S/N ratos. No. MRR Kerf SR RL [mm 3 /mn] S/N [mm] S/N [µm] S/N [µm] S/N Fgure-1. Optmzaton steps usng the Taguch-grey-fuzzy method. 2781

4 Determnaton of Optmal WEDM Process The S/N ratos that shown n Table-3 should be normalzed. Because the hgher value of S/N rato shows the better performance, the normalzaton can be obtaned usng the followng equaton [3]: x x ( k) mn x ( k) k) (3) max x ( k) mn x ( k) ( where preprocessng, k the mnmum value of x k s the sequence after data x s the orgnal sequence, mn x k s th x k for the k max x k s the maxmum value of k response. The nto grey relatonal coeffcent formula below [3]: response, and th x for the k x k of each response needs to be converted or GRC by usng the mn ( k) ( k), max max (4) where, k x k x k s the absolute value of dfference between x k and x k, x k s the reference sequence, x k s the comparatve sequence, s the mnmum value of mn, k x k x k, s the maxmum value of max, k max max k x k x k = mn mn k = and (,1) s the dstngushng coeffcent. In ths experment, the selected value of was, 5 [1]. The GRC of each response should be converted nto a sngle value by usng fuzzy logc method. Fuzzy logc has three basc components,.e., fuzzfer, nference engne and defuzzfer. As a result, a sngle value performance characterstc called grey fuzzy reasonng grade (GFRG) was generated. In ths research, the nput varables such as GRC of MRR, kerf, SR and RL have been represented by membershp functon wth three fuzzy subsets: small (S), medum (M) and large (L) (Fgure-2). The output varable (GFRG) s represented by membershp functon wth nne fuzzy subsets: tny (T), very small (VS), small (S), medum small (MS), medum (M), medum large (ML), large (L), very large (VL) and huge (H) (Fgure-3). The trangle membershp functon was used for both nput and output varables. Table-4 shows GRC and GFRG. The larger GFRG the better multple performances s. The mean GFRG for each level of the machnng parameters s shown n Table-5. Fgure-2. Membershp functons for nput parameter (a) MRR, (b) kerf, (c) SR, (d) RL. 2782

5 combnaton of machnng parameters of AN at level 1, ON at level 1, OV at level 1 and SV at level 1 (AN 1ON 1OV 1SV 1). Fgure-4 shows the grey-fuzzy reasonng grade (GFRG) graph. Table-5. Response table for the mean GFRG. Fgure-3. Membershp functons for output parameter GFRG. No. Table-4. GRC and GFRG. GRC MRR Kerf SR RL GFRG Based on Table-5, the maxmum MRR and mnmum kerf, SR and RL could be obtaned by Machnng parameters Delta AN ON OV SV Average.5315 Analyss of Varance and Confrmaton Experment In order to fnd out whch machnng parameters of WEDM process that sgnfcantly affect the multple machnng performance, analyss of varance (ANOVA) and F test are used to analyze the expermental data. The result of ANOVA n Table-6, shows that ON gves the hghest contrbuton for reducng the total varaton of the multple responses, followed by OV, SV and AN. When the combnaton level of machnng parameters whch result optmal machnng performance has been obtaned, the next step s predctng and verfyng the mprovement of the machnng performance. The predcted GFRG ˆ can be obtaned by usng followng equaton [12]: m q 1 m ˆ (5) where s the total mean of GFRG, m s the mean of GFRG taken at the optmal condton and q s the number of machnng parameters that sgnfcantly affect the multple machnng performance. Table-7 shows the comparson of the result of confrmaton experment usng the optmal machnng parameters and the result of the confrmaton experment usng the ntal machnng parameters. As shown n Table-7, the optmum settng level mproves the machnng performance. The kerf s decreased from.347 mm to.319 mm, SR s decreased from 1.91 µm to 1.37 µm and RL s decreased from µm to 4.86 µm. The GFRG s also greatly mproved through ths study by 61.45%. 2783

6 Fgure-4. GFRG graph. Table-6. Result of the ANOVA for GFRG. Source DF SS MS F P Contrbuton AN % ON % OV % SV % Error % Total % Table-7. Result of the confrmaton experment. Intal machnng parameter Optmal machnng parameter Predcton Experment Settng level AN 1ON 2OV 2SV 2 AN 1ON 1OV 1SV 1 AN 1ON 1OV 1SV 1 MRR [mm 3 /mn] Kerf [mm] SR [µm] RL [µm] GFRG Improvement n GFRG = % The Expermental Results and Confrmaton Test Show that: On tme gves the hghest contrbuton for reducng the total varaton of the multple responses n WEDM process, followed by open voltage, servo voltage and arc on tme. The maxmum materal removal rate and mnmum cuttng wdth, surface roughness and recast layer thckness could be obtaned by usng the values of arc on tme, on tme, open voltage and servo voltage of 1 A, 2 µs, 75 V and 3 V respectvely. A grey-fuzzy reasonng grade (GFRG) of.7874 was predcted and.8292 was obtaned expermentally. The GFRG greatly mproved through ths study by 61.45%. CONCLUSIONS The study has confrmed the use of grey relatonal analyss and fuzzy logc based on the Taguch method to optmze multple performance characterstc problem of WEDM operaton. As a result, the performance characterstcs such as cuttng wdth (kerf), surface roughness (SR) and recast layer thckness (RL) can be mproved through ths method. 2784

7 REFERENCES [1] S. Kalpakjan and S.R. Schmd. 29. Manufacturng process for engneerng materals. Pearson educaton, South Asa. grey relatonal analyss. Forty sxth CIRP Conference on Manufacturng System, [12] M.S. Phadke Qualty engneerng usng robust desgn. Pearson Educaton, South Asa. [2] N. Tosun, C. Cogun and G. Tosun. 24. A study on kerf and materal removal rate n wre Electrcal dscharge machnng based on Taguch method. J. Mater. Process. Technol. 152: [3] J.L. Ln, C.L. Ln. 25. The use of grey-fuzzy logc for the optmzaton of the manufacturng process. J. Mater. Process. Technol. 16: [4] Y.M. Pur and N.V. Deshpande. 24. Smultaneous optmzaton of multple qualty characterstcs of WEDM based on fuzzy logc and Taguch technque. Proc. of the Ffth Asa Pacfc Industral Engneerng and Management Systems Conference, [5] K. Jangra, S. Grover, A. Aggarwal Optmzaton of mult machnng characterstcs n WEDM of WC-5.3 % Co composte usng ntegrated approach of Taguch, GRA and entropy method. Front. Mech. Eng. 7(3): [6] B.O.P. Soepangkat and B. Pramujat Optmzaton of surface roughness and recast layer thckness n the wre-edm process of AISI D2 tool steel usng Taguch-grey-fuzzy. Appled Mechancs and Materals. 393: [7] B.O.P. Soepangkat, B. Pramujat and N. Lus Optmzaton of multple performance characterstcs n the wre EDM process of AISI D2 tool steel usng Taguch and fuzzy logc. Advanced Materals Research. 789: [8] J. Deng Introducton to grey system. J. Grey Syst. 1: [9] L. Zadeh Fuzzy sets. Inform. Control. 8: [1] G. Taguch Introducton to qualty engneerng. Asan Productvty Organzaton, Tokyo. [11] U.A. Dabade Mult-objectve process optmzaton to mprove surface ntegrty on turned surface of A1/SCp metal matrx compostes usng 2785