Optimization Of Multiple Performance Characteristics In Electro Discharge Machining Using Grey Relational Analysis

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1 Optmzaton Of Multple Performance Characterstcs In Electro Dscharge Machnng Usng Grey Relatonal Analyss Subhakanta Nayak, B.C.Routara Abstract: Ths paper dscusses the applcaton of the Taguch method to optmze the machnng parameters for machnng of tungsten carbde n electro-dscharge machnng (EDM) for ndvdual responses such as materal removal rate, electrode wear rate and surface roughness. Moreover, a mult-response performance characterstc was used for optmzaton of process parameters wth applcaton of grey relatonal analyss. The machnng parameters are selected as current, pulse-on-tme and pulse-off-tme where as the response varables selected as materal removal rate (MRR), electrode wear rate (EWR) and surface roughness (Ra). The results from confrmaton runs ndcated that the determned optmal combnaton of machnng parameters mproved the performance of the machnng process. Key words: ANOVA, EDM, EWR,Grey Relatonal analyss, MRR,Optmzaton, Surface Roughness, Taguch Method, 1. INTRODUCTION Electrcal Dscharge Machnng (EDM) s a non-tradtonal manufacturng process based on removng materal from a part by means of a seres of repeated electrcal dscharges (created by electrc pulse generators at short ntervals) between a tool, called electrode, and the part beng machned n the presence of a delectrc flud. Electrcal Dscharge Machnng (EDM) s a process that s used to remove metal through the acton of an electrc dscharge of short duraton and hgh current densty between the tool and the work pece. It has been proven to be especally valuable n the machnng of super-tough, electrcally conductve materals such as the new space-age alloys. These metals would have been dffcult to machne by conventonal methods, but EDM has made t relatvely smple to machne ntrcate shapes that would be mpossble to produce wth conventonal cuttng tools. Ths machnng process s contnually fndng further applcatons n metal machnng ndustry. In EDM, a potental dfference s appled between the tool and work-pece. Both the tool and the work materal are to be conductors of electrcty. The tool and the work materal are mmersed n a delectrc medum. Generally kerosene or de-onzed water s used as the delectrc medum. A gap s mantaned between the tool and the work-pece. Dependng upon the appled potental dfference and the gap between the tool and work-pece, an electrc feld would be establshed. Subhakanta Nayak, B.C.Routara College Of Engneerng, Mechancal Engneerng, Bhubaneswar Emal: subha.nayak2007@gmal.com 2. LITERATURE REVIEW Ths revew outlnes some of the recent reports publshed n lterature on machnng of dfferent materals n EDM to fnd out the effect of the nput parameters taken, on the responses. Lterature depcts that C.H. Che Haron et al. (8) has taken copper electrodes of dfferent dameters and done machnng n EDM of AISI 1045 tool steel at two current settngs and concluded that the materal removal rate and the electrode wear rate were not only dependent on the dameter of the electrode but also had close relaton wth the supply of current. Low current was found sutable for small dameter electrode, hgh current for bg dameter electrode. M. Kyak (9) has examned the machnng parameters on surface roughness n EDM of tool steel and has concluded that surface roughness ncreases wth ncrease of pulse current and pulse tme and ncreases materal removal rate. I. Puertas et al. (1) has concluded the same result n machnng WC-Co n EDM. Lau et al. (10) carred out an experment work to nvestgate the feasblty of EDM as a means of machnng carbon fber composte materals. The machnng was conducted at varous currents, pulse duratons and wth dfferent tool materals and polartes. It was clearly concluded that there exts an optmum materal removal rate wth peak current and pulse on tme. It was agan proved that copper electrodes perform better than graphte electrodes n terms of tool wear and surface fnsh. S.H. Lee (6) studed the nfluence of operatng parameters of EDM of tungsten carbde on the machnng characterstcs. The effectveness of the EDM process wth WC s evaluated n terms of materal removal rate, electrode wear rato and surface fnsh. They found that materal removal rate and surface fnsh of the work pece are drectly proportonal to the dscharge current ntensty. Son and Chakravert [4, 12] studed the surface qualty, materal removal rate, wear rato, and dmensonal accuracy n EDM of alloy steels. Sngh et al. nvestgated the effect of machnng parameters on electrode wear n desnkng EDM of En-31 tool steel wth dfferent electrode materals [5]. Also, Lus et al. have carred out a study on electrode wear n EDM of slcon carbde usng the technque of desgn of experments In 1989, Narumya et al. began to notce and examned the effects of slcon powder n the workng flud on EDM. They found that foregn partcles were capable of reducng the recast layer, preventng cracks, and producng a mrror lke surface fnsh n the EDMed 116

components. In 1993, Yan et al. contnued to nvestgate the effects of delectrcs wth suspended alumnum, alumna, and slcon carbde powders, respectvely on EDM.

Tsa and Wang developed a sem emprcal model n whch parameters affectng the surface roughness were dentfed to be spark tme, maxmum current, polarty, nput power, materal densty, conductvty of the

[13] suggested surface crackng can be reduced by ncreasng the pulse current or shortenng the pulse-on duraton.

2 components. In 1993, Yan et al. contnued to nvestgate the effects of delectrcs wth suspended alumnum, alumna, and slcon carbde powders, respectvely on EDM. They observed the same results as those produced by slcon powders. Tsa and Wang developed a sem emprcal model n whch parameters affectng the surface roughness were dentfed to be spark tme, maxmum current, polarty, nput power, materal densty, conductvty of the materal, specfc heat capacty, heat conductvty, meltng pont, and bolng pont of the materal. Ta et al.[13] suggested surface crackng can be reduced by ncreasng the pulse current or shortenng the pulse-on duraton. Furthermore, t s shown that steels wth a lower carbon equvalent ndex or a hgher thermal conductvty are more resstant to crack formaton. Bhattacharya et al.[14] set the parameter for rough and fnshed machnng operatons n EDM for EN31and exhbted maxmum MRR as compared to the other two materals such as H11 and HCHCr at smlar process settngs. Copper (Cu) electrode wth alumnum suspended n the delectrc maxmzed the MRR. The grey system theory ntated by Deng n 1982 has been proven to be useful for dealng wth poor, ncomplete, and uncertan nformaton. The grey relatonal analyss based on the grey system theory can be used to solve the complcated nterrelatonshps among the multple performance characterstcs effectvely. Through the grey relatonal analyss, a grey relatonal grade s obtaned to evaluate the multple performance characterstcs. As a result, optmzaton of the complcated multple performance characterstcs can be converted nto optmzaton of a sngle grey relatonal grade. In Grey relatonal analyss, the complex multple response optmzatons can be smplfed nto the optmzaton of a sngle response Grey relatonal grade. In the present study, the process parameters of EDM, vz., current (A), pulse-on-tme (µs) and pulse-off-tme (µs) are optmzed for ndvdual responses. Grey relatonal analyss s done to have optmum cuttng parameter combnaton that yelds desrable machnng characterstcs. Confrmaton experments are conducted to verfy the optmal cuttng parameter combnaton as predcted by grey relatonal analyss. ANOVA s also carred out to observe the level of sgnfcance of factors and ther nteractons on the overall grey relatonal grade. Results ndcate that ths approach s both effcent and effectve n determnng the optmal cuttng parameters on MRR, EWR and surface roughness. 3. EXPERIMENTAL DETAILS 3.1 Expermental set up Durng ths study, a seres of experments on EDM of tungsten carbde of sze 20mm x 20mm x 5mm was conducted on a ECOWIN MIC-432C electrcal dscharge machne to examne the effects of nput machnng parameters such as pulse on tme, pulse off tme and current, on the materal removal rate and electrode wear rate and surface roughness. The electrode taken was 99.9% copper rod of dameter 8 mm. Separate copper electrode was taken for each experment. Paraffn was used as delectrc flud n ths expermentaton. The machnng tests were carred out wth a total tme of 10 mnutes for each experment. The pressure and temperature of the delectrc flud was assumed to be constant throughout the experment. Materal s removed durng machnng from both the work pece and the tool by the erosve acton of electrcal dscharges. The materal removed from the work pece and the tool then determned from ther respectve weght dfferences before and after undergong EDM process. Ths s then dvded by respectve denstes n order to convert t nto volumetrc term and further dvded by the actual tme of machnng to obtan the materal removal rate and electrode wear rate. The surface roughness s measured by stylus type proflometer (Taylor s Hobson Surtronc +25). 3.2 Tool materal & Work-pece materal used The electrode materal used durng ths study was electrolytc copper rod of dameter 8mm havng 99.9% copper as shown n Fg.1. The work-pece materal s tungsten carbde as shown n Fg.2. Fg.1 Electrodes used n experment Fg.2 Tungsten Carbde Plates 3.3 Machne tool The ECOWIN 432C machne shown n Fg.3 s a de-snkng machne manufactured by Ecown Corporaton, Tawan. It s energzed by a 60 A pulse generator. Paraffn was used as delectrc flud durng the experments. All the machnng work has been done n ths machne takng separate copper electrode and tungsten carbde as work-pece for ndvdual experments. Fg.3 ECOWIN MIC-432C EDM 117

3 3.4 Selecton of process parameters The selected process parameters are: Current (A), Pulse-ontme (µs) and Pulse-off-tme (µs). 3.5 Selecton of response varable Materal removal rate (MRR) It represents the volume of materal removed from the work pece n unt tme. Volumeof materalremovedfrom part MRR Tmeof machnng Electrode Wear Rate (EWR): It s defned as the volume of materal eroded from the tool electrode per unt tme dvded by the volume of materal eroded from the work pece n the same tme. EWR Volumeof materalremoved from electrodeperunttme volumeof materalremovedfrom work peceperunttme Surface roughness (Ra) The centre lne average surface roughness (Ra) of the machned surface was measured usng a stylus type surface roughness tester (Taylor Hobson, Surtronc +25) of samplng length 0.8 mm, evaluaton length of 4 mm and least count of 0.01 μm. The result of the surface roughness depends on the stylus path drecton. For ths reason the roughness were measured four tmes and averaged and expressed n mcrons (μm). 3.6 Desgn of experments As 3 process parameters wth 3 levels are selected, the total number of experments as per full factoral desgn should be 27. Taguch proposes the L 9 orthogonal array for three level three factor desgn whch corresponds to only 9 experments. Ths reduces the machnng tme and cost. The process varables (desgn factors) wth ther values on dfferent levels are lsted n Table 1. The expermental layout for the three process parameters usng the L 9 orthogonal array s shown n Table 2. Table 3 shows the expermental results after machnng. Table 1.Factors and levels of process parameters Factors Symbols Levels Current (A) Pulse on tme(µs) Pulse off tme(µs) A B C Table 2.Expermental layout usng an L 9 orthogonal array Exp. No: A Current (A) B Pulse on tme (µs) C Pulse off tme (µs) Exp. No: Table 3 Expermental Results MRR (mm 3 /mn) EWR (%) R a (µm) GREY RELATIONAL ANALYSIS Grey relatonal analyss owes ts orgn to grey system theory. Any system n nature s not whte (full of precse nformaton), but on the other hand, t s not black (completely lack of nformaton) ether, and t s mostly grey (a mxture of black and whte). The ncompleteness of nformaton s the basc characterstc and t serves as the fundamental startng pont of the nvestgaton of grey system. Incomplete nformaton follows from the lmted avalablty of data and the central problem of grey system theory s to seek only the ntrnsc structure of the system gven such lmtaton of data. The man objectve of grey system theory s to supply nformaton so that one can whten the greyness. Grey relatonal analyss s based on the grey system theory, and compares and computes the dynamc causaltes of the subsystems of a gven system. For better performance of a machnng process the MRR should be hgh whereas the surface roughness and EWR should be low. Thus, t s a case of mult response optmzaton, whch s dfferent from that of a sngle performance characterstc. The hgher S/N rato for one performance characterstc may correspond to a lower S/N rato for another. Therefore, the overall evaluaton of the S/N rato s requred for the optmzaton of multple performance 118

4 characterstcs. In the grey relatonal analyss, data preprocessng s frst performed n order to normalze the raw data (Table 4) for analyss. In ths study, a lnear normalzaton of the expermental results for MRR, EWR and surface roughness were performed n the range between zero and one, whch s also called the grey relatonal generaton. In the EDM operaton, the materal removal rate should be hgher. Therefore the normalzed data processng for the materal removal rate s the hgher-the-better performance characterstcs consdered and s expressed as x ( y( mn y( max y ( mn y ( (1) In the grey relatonal analyss, the normalzed data processng for electrode wear rate (EWR) and surface roughness (R a ) correspondng to lower-the-better crteron s expressed as x ( max y( y( max y ( mn y ( (2) Eq (1) s used for the larger-the-better responses and Eq (2) s used for the lower-the- better responses, where x ( s the value after grey relatonal generaton, mn y ( s the smallest value of y ( for the k th response, and the max y ( s the largest value of y ( for the k th response. An deal sequence s x o ( where (k= 1, 2, 3 for MRR, EWR and R a respectvely). The defnton of grey relatonal grade n the course of grey relatonal analyss s to reveal the relatonal degree between the nne sequences [x o ( and x (, =1, 2,3..9]. The grey relatonal coeffcent k can be calculated as mn max ( (3) ( o o o max Where x ( x ( =dfference of the absolute value between x o ( and x (; Ψ= dstngushng coeffcent between zero and one, the purpose of whch s to weaken the effect of when t gets too bg, and thus enlarges the max dfference sgnfcance of the relatonal coeffcent. In the present case, = 0.5 s used. Table 4 Data processng of each performance characterstc (Grey relatonal generaton) Exp. No MRR EWR Ra Ideal Sequence Then the overall grey relatonal grade s calculated by averagng the grey relatonal coeffcent correspondng to each selected process response usng Eq.(4). The overall grey relatonal grade s shown n Table n ( (4) n k 1 Where n = number of process responses. The hgher value of grey relatonal grade s consdered as the stronger relatonal degree between the deal sequence x 0 ( and the gven sequence x (. It has already been mentoned that the deal sequence x 0 ( s the best process response n the expermental layout. Thus the hgher grey relatonal grade mples that the correspondng parameter combnaton s closer to the optmal. Table5. Grey relatonal grade of performance characterstc Exp. No Grade The hgher grey relatonal grade represents that the correspondng expermental result s closer to the deally normalzed value. In other words, optmzaton of the complcated multple performance characterstcs can be converted nto optmzaton of a sngle grey relatonal grade. The grey relatonal grade s shown n the Table 5. Snce the expermental desgn s orthogonal, t s then possble to separate out the effect of each machnng parameter on the grey relatonal grade at dfferent levels. Bascally, the larger the grey relatonal grade, the better s the multple performance characterstcs. However, the relatve mportance among the machnng parameters for the multple performance characterstcs stll needs to be known so that the optmal combnatons of the machnng parameter levels can be determned more accurately. ANOVA for grey relatonal grade The purpose of the analyss of varance (ANOVA) s to nvestgate whch machnng parameters sgnfcantly affect the performance characterstc. Ths s accomplshed by separatng the total varablty of the grey relatonal grades, whch s measured by the sum of the squared devatons from the total mean of the grey relatonal grade, nto contrbutons by each machnng parameter and the error. In addton, the Fsher's F-test can also be used to determne whch machnng parameters have a sgnfcant effect on the performance characterstc. Usually, the change of the machnng parameter has a sgnfcant effect on the 119

performance characterstc when F s large s shown n Table 6. Table 6 ANOVA for Grey relatonal grade Source df SS MS F P A 2 0.1082 0.0541 2027.6 0.000 B 2 0.0023 0.0011 42.89 0.023 C 2 0.0625 0.

5 performance characterstc when F s large s shown n Table 6. Table 6 ANOVA for Grey relatonal grade Source df SS MS F P A B C Error Total In the grey relatonal grade graph (Fg.3), t s clearly mentoned that frst level of current, second level of pulse on tme, frst level of pulse off tme (A 1 B 2 C 1 ) are the optmal combnaton of process parameters for multple performance characterstcs. EDM process such as materal removal rate and surface fnsh are mproved together by usng ths approach. Table 7 Results of the confrmaton tests Intal cuttng parameters Optmal cuttng parameters Predcton Experment Settng Level MRR EWR Surface Roughness Grey relatonal grade A 1 B 1 C 1 A 1 B 2 C 1 A 1 B 2 C Improvement of grey relatonal grade = CONCLUSION The use of the orthogonal array wth grey relatonal analyss to optmze the EDM process wth the multple performance characterstcs has been reported here. A grey relatonal analyss of the expermental results of MRR, EWR and surface roughness can convert optmzaton of a sngle performance characterstc called the grey relatonal grade. As a result, optmzaton of the complcated multple performance characterstcs can be greatly smplfed through ths approach. It s shown that the performance characterstcs of the EDM process such as MRR, EWR and surface roughness are mproved together by usng ths approach. Fg 3.Grey relatonal grade graphs of multple performance characterstcs Confrmaton Tests After the optmal level of machnng parameters has been dentfed, a verfcaton test needs to be carred out n order to check the accuracy of analyss. The estmated grey relatonal grade,ˆ, usng the optmal level of the process parameters can be calculated as: o m ˆ (5) m 1 where m s the total mean grey relatonal grade, s the mean grey relatonal grade at the optmal level, and o s the number of the man desgn parameters that sgnfcantly affect the machnng characterstcs of EDMed surfaces. Table 7 shows the comparson of the estmated grey relatonal grade wth the actual grey relatonal grade obtaned n experment usng the optmal cuttng parameters. It may be noted that there s good agreement between the estmated value ( ) and expermental value ( ). The mprovement of grey relatonal grade from ntal parameter combnaton (A 1 B 1 C 1 ) to the optmal parameter combnaton (A 1 B 2 C 1 ) s Ths s about 28% of the mean grey relatonal grade and thus there s sgnfcant mprovement. Here, t may conclude that the multple performance characterstc of the REFERENCES [1]. Puertas I., Lus C.J., Álvarez L., Analyss of the nfluence of EDM parameters on surface qualty, MRR and EW of WC Co, Journal of. Materal. Processng. Technology , , [2]. Routara B.C., Sahoo P. and Bandyopadhyay A., Parametrc optmzaton of EDM process parameters usng grey relatonal analyss for multple performance characterstc, Internatonal Journal for Manufacturng Scence and Producton, 8, , [3]. P.J. Ross, Taguch technques for Qualty Engneerng, New York, Mc-Graw-Hll, 1988 [4]. Son J.S., Chakravert G, Effect of electrode materal propertes on surface roughness and dmensonal accuracy n electro-dscharge machnng of hgh carbon hgh chromum de steel, J. Ind. Eng. 76, 46 51, [5]. Sngh S., Maheshwar S., Pandey P.C., Some nvestgatons nto the electrc dscharge machnng of hardened tool steel usng dfferent electrode materals, Journal of Materal Processng Technology 149, , [6]. Lee, S.H., L, X.P. Study of the effect of machnng parameters on the machnng characterstcs n EDM 120

6 of tungsten Carbde, Journal of. Materal. Processng. Technology, 115, , [7]. Soo Hong Lee, Xaopng L, Study of the surface Integrty of the machned work pece n the EDM of tungsten Carbde, Journal of. Materal. Processng. Technology, 139, , [8]. C.H. Che Haron, B. Md. Deros, A. Gntng, M. Fauzah, Investgaton on the nfluence of machnng parameters when machnng tool steel usng EDM, Journal of. Materal. Processng. Technology, 116, 84-87, [9]. Kyak M., Cakır O., Examnaton of machnng parameters on surface roughness n EDM of tool steel Journal of. Materal. Processng. Technology, 191, , [10]. W. S Lau, M. Wang, W.B. Lee, Electrcal dscharge machnng of carbon fber composte materals, Int J Mach Tools Manuf, 30(2); , 1990; [11]. Taguch G., Introducton to Qualty Engneerng, Tokyo; Asan Productvty Organzaton, [12]. Son J.S., Chakravert G., Investgatve study on metal removal rate and wear rato n EDM of hgh carbon hgh chromum de steel, J. Ind. Eng. 71, [13]. Tzu-Yao Ta & S. J. Lu & Y. H. Chen, Surface crack susceptblty of electro dscharge-machned steel surfaces Internatonal Journal of Advanced Manufacturng Technology, 57, , [14]. Bhattacharya A., Batsh A., Sngh G., Sngla V. K., Optmal parameter settngs for rough and fnsh machnng of de steels n powder-mxed EDM, Internatonal Journal of Advanced Manufacturng Technology, 61, ,

Optimization of machining parameters of EDM while machining Inconel 718 for form tolerance and orientation tolerance

Optimization of machining parameters of EDM while machining Inconel 718 for form tolerance and orientation tolerance Indan Journal of Engneerng & Materals Scences Vol. 2, October 213, pp. 391-397 Optmzaton of machnng of EDM whle machnng Inconel 718 for form tolerance and orentaton tolerance S Dhanabalan a, K Svakumar