MULTI OPTIMIZATION OF PROCESS PARAMETERS BY USING GREY RELATION ANALYSIS- A REVIEW

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1 MULTI OPTIMIZATION OF PROCESS PARAMETERS BY USING GREY RELATION Mhr Thakorbha Patel* ANALYSIS- A REVIEW Abstract: In ths paper, a comprehensve and n-depth revew on mult optmzaton of dfferent process parameters were carred out usng Grey relaton analyss. Qualty and productvty are two of the most mportant crtera n any machnng operaton. But t can be seen that there s tradeoff between them. So, t s essental to optmze qualty and productvty smultaneously. The grey relaton analyss s the effectve methodology to optmze multple performance parameters. The selecton of the process parameters s also mportant for any optmzaton of requred qualty characterstcs. The grey relaton analyss s not used to optmze machnng parameters only. It also used to software evaluaton, ste selecton, bank selecton etc. The grey relaton analyss shows the optmal sequence whch gves better results than theoretcal predcated or conventonally selected sequence. Keywords: ANOVA, Grey relaton analyss, Mult optmzaton, Process parameters, Taguch. *Lecturer, Department of Mechancal Engneerng, BBIT, V.Vnagar, Gujarat Vol. 4 No. 6 June IJARIE 1

2 INTRODUCTION The qualty of a product s the mportant factor for showng growth of a company. Qualty and productvty are two of the most mportant crtera n any machnng operaton. But t can be seen that there s tradeoff between them.e. as the qualty ncreases then productvty tends to decrease. It s therefore essental to optmze qualty and productvty smultaneously. Optmzaton technque plays a vtal role to mprove the qualty of the product. The product beng machned has to have the mnmum surface roughness and n order to obtan hgh qualty. On other sde the processng tme has to be compromsed whch drectly affects the productvty. It s very mportant to optmze both the factors smultaneously. In such a case mult objectves optmzaton are necessary to solve the above problem. There are varous methods are used for mult optmzaton lke grey relaton analyss, utlty concept etc. GREY RELATION ANALYSIS The grey relatonal analyss, whch s useful for dealng wth poor, ncomplete and uncertan nformaton, can be used to solve complcated nter-relatonshps among multple performance characterstcs satsfactorly. Followng are the steps needed for convertng the mult-response characterstcs to sngle response characterstcs [4]. 1. Normalze the expermental results of metal removal rate and surface roughness (data preprocessng) 2. Calculate the Grey relatonal co-effcent. 3. Calculate the Grey relatonal grade by averagng the Grey relatonal co-effcent. In the grey relatonal analyss, the expermental results are frst normalzed n the range between zero and unty. Ths process of normalzaton s known as the grey relatonal generaton. After then the grey relatonal coeffcent s calculated from the normalzed expermental data to express therelatonshp between the desred and actual expermental data. Then, the overall grey relatonal grade s calculated by averagng the grey relatonal coeffcent correspondng to each selected process response. The overall evaluaton of the multple process responses are based on the grey relatonal grade. Ths method converts a multple response process optmzaton problem wth the objectve functon of overall grey relatonal grade. The correspondng level of parametrc combnaton wth hghest grey relatonal grade s consdered as the optmum process parameter. Vol. 4 No. 6 June IJARIE 2

3 If the target value of the orgnal sequence s the-larger-the-better, then the orgnal sequence s normalzed usng below mentoned equaton. y (k) mn y (k) X j (k) max y (k) mn y (k) If the target value of requred purpose s the-smaller-the-better, then the orgnal sequence s normalzed usng below mentoned equaton. max y (k) y (k) X j (k) max y (k) mn y (k) where, x (k) and x j (k) are the value after Grey Relatonal Generaton for Larger the better and Smaller the better crtera. maxy (k) s the largest value of y (k) for k th response and mn y (k) s the mnmum value of y (k) for the k th response. The Grey relatonal coeffcent ξ (k) can be calculated as below mentoned equaton. mn max (k) 0 k max and o x 0 (k) x (k) Where o s the dfference between absolute value x 0 (k) Vol. 4 No. 6 June IJARIE 3 and x (k) and the dstngushng or dentfcaton coeffcent defned n the range 0= ξ =1 (the value may be adjusted based on the practcal needs of the system). The value of s the smaller, and the dstngushed ablty s the larger. =0.5 s generally used. After the grey relatonal coeffcent s derved, t s usual totake the average value of the grey relatonal coeffcents as the grey relatonal grade. The grey relatonal grade s defned as follows: 1 n (k) k n 1 Where ns the number of process responses. The hgher value of grey relatonal grade s consdered as the stronger relatonal degree between the deal sequence x 0 (k) and the gven sequence x (k). The hgher grey relatonal grade mples that the correspondng parameter combnaton s closer to the optmal. Sometmes grey relaton performed wth Taguch, t s also known as Taguch Grey relaton analyss. In that analyss followng steps to be performed [8]: 1. Normalzng the expermental results of requre response characterstcs.

4 2. Performng the Grey relatonal generatng and to calculate the Grey relatonal coeffcent. 3. Calculatng the Grey relatonal grade by averagng the Grey relatonal coeffcent. 4. Performng statstcal analyss of varance (ANOVA) for the nput parameters wth the Grey relatonal grade and to process. 5. Selectng the optmal levels of process parameters. fnd whch parameter sgnfcantly affects the 6. Conductng confrmaton experment and verfy the optmal process parameters settng. LITERATURE REVIEW AbhjtSaha et al. [1] were nvestgated mult response optmzaton of turnng process for an optmal parametrc combnaton to yeld the mnmum power consumpton, surface roughness and frequency of tool vbraton usng a combnaton of Grey relatonal analyss (GRA). Confrmaton test was also conducted for the optmal machnng parameters to valdate the test result. They have taken turnng parameters, such as spndle speed, feed and depth of cut. Experments were desgned and conducted based on full factoral desgn of experment. Abhshek Dubey et al. [2] were worked on multple response optmzaton of end mllng parameter usng grey based Taguch method. Experments were desgned and conducted based on L 27 orthogonal array desgn. The mllng parameter were spndle speed, depth of cut, feed rate and pressurzed coolant jet and the response was surface roughness. They concluded that the spndle speed was the most nfluental control factor among the all process parameters for mnmzaton of surface roughness. Arun Kumar Parda et al. [3] had optmzed the machnng parameters n turnng of glass fber renforced polymer (GFRP) compostes on all geared lathe machne. They had taken spndle speed, feed rate and depth of cut as machnng parameters, surface roughness and materal removal rate as a response parameters. Taguch s L 9 orthogonal array has been used for perform experments. Analyss of varance has been carred out to check the sgnfcant process parameter n a sngle objectve performance characterstc. They concluded that performance characterstc of the machnng process such as MRR and surface roughness are mproved together by usng ths approach. Vol. 4 No. 6 June IJARIE 4

5 B. Shvapragash et al. [4] had studed effect of dfferent process parameters on materal removal rate and surface roughness on Al-TBr materal on radal drllng machnng wth dry condtons. They analyzed the results usng Grey relatonal analyss. They found for mult optmzaton that best combnaton of the cuttng parameters was the set wth spndle low speed, hgh feed rate and mddle depth of cut. Chao-Leh Yang [5] has carred out the mult optmzaton n the cuttng of glass fber. They found optmal process parameters for gven performance characterstcs at hghest cuttng speed and the smallest cuttng volume, and the medum cuttng load. They used L 9 Taguch orthogonal array for performng the experments. The optmal settng for multple performance characterstcs was found at hghest cuttng speed and the smallest cuttng volume, and the medum cuttng load. They also found from analyss of varance that cuttng speed was the most contrbutng parameters and cuttng load was least contrbutng parameter for multple response parameters. They also performed confrmaton experments for valdate the optmal results. They also concluded that Grey-based Taguch methods s a good way to mprove the multple performance parameters. Chh-Hung Tsa et al. [6] had appled grey relaton analyss for proper vendor selecton. They have taken defect, quotaton, delay rate, shortage rate and score as nput parameters. They found that grey relaton methodology sgnfcantly reduced the purchasng cost and ncrease the producton effcency and overall compettveness. D. Chakradharet al. [7] were performed mult optmzaton for Electrochemcal machnng on EN31 steel by Grey Relatonal Analyss. The process parameters consdered are electrolyte concentraton, feed rate and appled voltage and are optmzed wth consderatons of multple performance characterstcs ncludng materal removal rate, overcut, cylndrcty error and surface roughness. Funda OZCELIK et al. [8] had evaluated banks sustanablty performance n Turkey by usng Grey relatonal analyss. Banks performances have been analyzed based on 3 fnancal, 2 socal and 4 envronmental ratos and banks have been lsted based on ther sustanablty performance. Accordng to the sustanablty performance of banks, TSKB ranks frst and s followed by Garant Bank and Akbank respectvely. Geeta Nagpalet al. [9] had used grey relaton analyss and fuzzy logc technques for software estmaton. Two alternatve approaches usng analogy for estmaton have been Vol. 4 No. 6 June IJARIE 5

6 proposed n ths study. Frstly, a precse and comprehensble predctve model based on the ntegraton of Grey Relatonal Analyss (GRA) and regresson has been dscussed. Second approach deals wth the uncertanty n the software projects, and how fuzzy set theory n fuson wth grey relatonal analyss can mnmze ths uncertanty. Hossen Hasanet al. [10] had performed grey relatonal analyss for optmzng the process parameters for open-end spun yarns. The raw materals used n ths nvestgaton were cotton fbers (35%) and cotton waste (65%) collected from gnnng machnes. They concluded that grey relatonal analyss and the Taguch Method can be applcable for the optmzaton of process parameters and help to mprove process effcency. J.T. Huang et al. [11] had appled to determne the sutable selecton of machnng parameters for Wre Electrcal Dscharge Machnng (Wre-EDM) process usng grey relaton analyss. They found that the table-feed rate has a sgnfcant nfluence on the machnng speed, whlst the gap wdth and the surface roughness are manly nfluenced by pulse-on tme. Moreover, the optmal machnng parameters settng for maxmum machnng speed and mnmum surface roughness can be obtaned. Kamal Jangraet al. [12] had optmzed materal removal rate and surface roughness smultaneously; grey relatonal analyss was employed along wth Taguch method. Analyss of varance (ANOVA) had shown that the taper angle and pulse-on tme are the most sgnfcant parameters affectng the multple machnng characterstcs. Confrmatory results, proves the potental of GRA to optmze process parameters successfully for multmachnng characterstcs. M. S. Reza et al. [13] were optmzed mult-performance optmzaton characterstcs on Electrcal Dscharge Machnng njecton flushng type control parameters by usng Grey Relatonal Analyss. The machnng parameters selected were polarty, pulse on duraton, dscharge current, dscharge voltage, machnng depth, machnng dameter and delectrc lqud pressure. Results shown that machnng performance was mproved effectvely usng ths approach. Meenu Gupta et al. [14] had performed dfferent experments (usng mxed L 18 orthogonal array) to optmze process parameters on undrectonal glass fber renforced plastc materal usng NH 22 HMT lathe. They have analyzed the results data usng Taguch method and Grey relatonal analyss. They found that depth of cut was the factor, whch has great Vol. 4 No. 6 June IJARIE 6

7 nfluence on surface roughness and materal removal rate, followed by feed rate. The percentage contrbuton of depth of cut was % and feed rate was 5.355%. MehulA.Ravalet al. [15] had performed experments on abrasve water jet machne on AISI steel materal and analyzed the results usng grey relaton analyss. They have selected abrasve gran, pressure, tp dstance and pole dstance as the process parameters and materal removal rate and surface roughness as qualty parameters. They selected Taguch L 9 orthogonal array to conduct experments. They concluded that the optmum condton for mult response parameters was meetng at abrasve gran sze (A1), pressure (B2), tp dstance (C3) and pole to work pece dstance (D1). P. Narender Sngh et al. [16] had used grey relaton analyss to optmze mult performance characterstcs lke MRR, TWR, Taper, Radal over cut, and surface roughness for current, pulse on tme, flushng pressure process parameters. They found that current at level 3, pulse on-tme at level 3 and the flushng pressure at level 1 for maxmzng MRR and for mnmzng TWR, T, ROC and SF gven an optmum machnng condtons for the requred output. The expermental result for the optmal settng shows that there was consderable mprovement n the process. Pankaj Sharma et al. [17] have tred to nvestgate the effect of varous process parameters (Cuttng Speed, Feed & Depth of cut) on materal removal rate and surface roughness on CNC TC. Taguch s L 18 orthogonal array used for performs experments. Analyss of varance (ANOVA) used to determne the sgnfcance of process parameters for ndvdual qualty parameter. The mult optmzaton was done by Grey relatonal analyss approach. They concluded that the performance characterstcs for the turnng operatons, such as the materal removal rate and the surface roughness are greatly enhanced by usng ths method. They found the optmal settng for mult objectve qualty parameters was V5-F2-D3. Raghuraman S. et al. [18] have tred to nvestgate effect of dfferent process parameters (Dscharge current, Pulse ON tme & Pulse OFF tme) on dfferent performance parameters (MRR, TWR, SR) n EDM. They performed experments usng L 9 orthogonal array, analyss has been carred out usng Grey Relatonal Analyss and Taguch method. The confrmaton experments were carred out to confrm the optmal results. Ther results show that the Taguch Grey relatonal Analyss s beng effectve technque to optmze the machnng parameters for EDM process. Vol. 4 No. 6 June IJARIE 7

8 Reddy Sreenvasuluet al. [19] had studed, the effects of drllng parameters on surface roughness and roundness error were nvestgated n drllng of AI6061 alloy wth HSS twst drlls. The obtaned expermental results were analyzed by Taguch Grey relaton analyss. They had taken Cuttng speed, feed rate, drll dameter, pont angle and cuttng flud mxture rato as control factors. They found that mnmum surface roughness and roundness error were obtaned wth treated drlls at m/mn cuttng speed and 0.3 mm/rev feed rate,10mm drll dameter, 110 degrees pont angle and 12% cuttng flud mxture rato. S V Subrahmanyamet al. [20] had tred to demonstrate the optmzaton of Wre Electrcal Dscharge Machnng process parameters for the machnng of H13 Hot De Steel, wth multple responses materal removal rate, surface roughness based on the Grey Taguch Method. They used Taguch L 27 (2 1 x3 8 ) orthogonal to conduct experments. They had taken eght process parameters TON, TOFF, IP, SV WF, WT, SF, WP each to be vared n three dfferent levels. They used grey relaton analyss to obtaned optmal sequence for mult qualty parameters. They found that the materal removal rate was ncreased from mm 3 /mn to mm 3 /mn and the Surface Roughness was reduced from 2.11µm to 2.01µm respectvely. They have also presented the mathematcal model for ndvdual qualty parameter. Semra BIRGUN et al. [21] have appled grey relaton analyss approach for call center ste selecton. They have taken nto consderaton both qualtatve and quanttatve factors and also s one of the most mportant strategc decsons affectng organzatons n terms of busness success. They exemplfed problem by applyng on a part of a project for a call center ste selecton by usng one of the mult-crtera decson-makng methods, namely herarchy grey relatonal analyss, based on applcaton of analytc herarchy process and grey relatonal analyss methods. The frst part they appled conventonal analytc herarchy process to determne the relatve weghts of the crtera. And the second part they appled grey relatonal analyss to rank the alternatves and then selects the optmum ste for call center. Shunmugesh K. et al. [22] had performed 27 experments (usng Taguch L 27 orthogonal array) to dentfy the effect of speed, pont angle and feed on surface roughness and delamnaton factor on drllng machne. They had taken glass fber renforced polymer as work materal. They have used grey relatonal analyss to analyze the results data. They Vol. 4 No. 6 June IJARIE 8

9 found the optmal settng for mnmzng surface roughness and delamnaton factor was spndle speed 1000 rpm, pont angle 135º and feed rate 0.5 m/mn. T Muthuramalngamet al. [23] have used Taguch grey relaton analyss approach for mult response optmzaton: maxmze the materal removal rate and mnmze the surface roughness n electrcal dschargng machne. They have taken gap voltage, peak current, and duty factor as nput process parameters. They found that peak current was the most sgnfcant parameters n electrcal dschargng machne. They also performed conformaton experments for valdate the expermental results. Durng the confrmaton runs, value of materal removal rate and surface roughness were and 6.42 respectvely. The grey relaton grade value durng the confrmaton experments was mproved by 1.7% from the predcated mean value. T V K Gupta et al. [24] had tred to demonstrate the optmzaton of abrasve water jet machnng process parameters for the machnng of SS 304 materal, wth multple responses surface roughness, taper, mpact force, vbraton & depth and wdth of cut based on the Grey Taguch Method. They have taken reverse speed, abrasve flow rate, abrasve sze and standoff dstance as process parameters. Based on the grey coeffcents and grades of the expermental data, a traverse speed of 3000 mm/mn, a dameter of mm of abrasve partcle at 0.49 kg/mn abrasve flow rate and a standoff dstance of 4 mm gven an optmum machnng condtons for the requred output. V. Chttaranjan Das et al. [25] have conducted experments based on Taguch L 9 orthogonal array to nvestgate the effect of process parameters current, open voltage, pulse duraton, duty factor on mult response characterstcs lke materal removal rate, tool wear rate and surface roughness n EDM. The valdaton experments results had shown the machnng performance of the materal removal rate ncreases from 2.92 to 3.69 mg/mn, the electrode wear rato decrease from 0.13 to 0.10mg/mn and the surface roughness decreases from 2.21 to 1.93µm, respectvely. Vol. 4 No. 6 June IJARIE 9

10 Table 1 Summary of dfferent revew papers Sr. No. Year Author Materal Machne Input Parameters Respondng Parameters AbhjtSaha and N.K.Mandal IS: 2062, Gr. B. Mld HSS MIRANDA Power consumpton, Ra & Spndle speed, Feed & DOC steel S400 (AISI T 42) Frequency of tool vbraton AbhshekDubey, Devendra Pathak, Nlesh Chandra, DOC, Feed, Spndle Speed and Pressurzed coolant EN31 steel Mllng machne Surface roughness AjendraNath Mshra and jet Rahul Davs Arun Kumar Parda, Rajesh Kumar Bhuyan and Bharat Chandra Routara B.Shvapragash, K.Chandrasekaran, C.Parthasarathy and M.Samuel GFRP compostes All geared lathe spndle speed, feed rate and depth of cut Al-TBr2 Drllng machne spndle speed, feed rate and depth of cut Materal removal rate and Surface roughness Materal removal rate and Surface roughness Chao-Leh Yang Glass fber materals Cuttng machne Cuttng Speed, Cuttng volume & Cuttng load Wear and Webull modulus Qualty, Prce, Chh-Hung Tsa, Chng-Lang Delvery date, Defect, Quotaton, Delay rate, Shortage rate and - Vendor Selecton Chang and Leh Chen Quantty and Score Servces D. Chakradhar, A. VenuGopal EN31 steel ECM Electrolyte conc., Feed rate & Voltage MRR, Overcut, Cyldrcty Error, Ra Funda OZCELIK &Burcu AVCI Economc Crtera, Envronmental Crtera & Socal - Bank Evaluaton of Bank OZTURK Crtera GeetaNagpal, Mon Uddn and Arvnder Kaur Software estmaton HossenHasan, Open-end spun Yarn count, Rotor Speed, Opened Speed, Navel CV %, har number per meter, SomayehAkhavanTabatabae, Cotton fbers yarns Speed and tenacty of yarn GhafourAmr 11 - J.T. Huang and Y.S. Lao SKD11 alloy steel EDM Pulse-on tme, Pulse-off tme, Table-feed rate, Materal removal rate, Gap Wre tenson, Wre velocty and Flushng pressure wdth & Surface roughness Kamal Jangra, Sandeep Grover WC-Co Composte Taper angle, Peak Current, Pulse-on tme, Pulse-off Materal removal rate and EDM and Aman Aggarwal materal tme, Wre Tenson & Delectrc flow rate Surface roughness M. S. Reza, M. Hamd and M. AISI 304 Stanless EDM Polarty, Pulse on duraton, Dscharge Current, Materal removal rate, Electrode Vol. 4 No. 6 June IJARIE 10

11 Sr. No. Year Author Materal Machne Input Parameters Respondng Parameters A. Azmr Steel Dscharge Voltage, Machnng Depth, Machnng wear rato and Surface Dameter & Delectrc pressure roughness Fber renforced Meenu Gupta and Surnder Tool nose Radus, Tool Rake angle, Feed rate, Materal removal rate and plastc composte NH 22 HMT lathe Kumar Cuttng speed, Cuttng envronment & DOC Surface roughness rods Mehul.A.Raval and Chrag. P. Abrasve gran sze, Pressure, Tp dstance, Pole Materal removal rate and AISI Steel water jet machne Patel dstance Surface roughness P. Narender Sngh, K. Al 10%SCP MRR, TWR, Taper, Radal over EDM Current, Pulse on tme, Flushng pressure Raghukandan& B.C. Pa compostes cut, and SR Pankaj Sharma, HMT Stallon-100 Materal removal rate and AISI H13 Cuttng Speed, Feed & Depth of cut KamaljeetBhambr HS CNC Surface roughness Materal removal rate, Tool Raghuraman S, Thruppath K, Panneerselvam T & Santosh S Mld Steel IS 2026 EDM Dscharge current, Pulse ON tme & Pulse OFF tme wear rate and Surface roughness Reddy Sreenvasulu and Cuttng speed, feed rate, drll dameter, pont angle Surface roughness and Al6061 alloy Drllng Machne Dr.Ch.SrnvasaRao and cuttng flud mxture rato Roundness error Pulse On tme, Pulse Off tme, Peak Current, Spark S V Subrahmanyam and M. gap Voltage Settng, Materal removal rate and H13 HOT DIE STEEL EDM M. M. Sarcar Wre tenson settng, Wre Feed rate settng, Servo Surface roughness Feed Settng, Flushng pressure of delectrc flud Semra BIRGUN, Cengz - GUNGOR - HRM, Economc, Regonal condton Call Center Ste Selecton Shunmugesh K, Glass Fbre Panneerselvam. K and Jospaul Drllng machne Renforced Polymer Thomas T Muthuramalngam and B. AISI 202 Stanless EDM Mohan Steel T V K Gupta, J Ramkumar, PuneetTandon& N S Vyas V.Chttaranjan N.V.V.S.Sudheer SS 304 materal Metal Das, compostes (Al/5%TCp) matrx EDM Abrasve water jet machnng Speed, Pont Angle and Feed Gap voltage, Peak current, Duty factor Reverse speed, abrasve flow rate, abrasve sze and standoff dstance Current, Open voltage, Pulse duraton, Duty factor Surface roughness Delamnaton Factor Materal removal rate and Surface roughness Surface roughness, Taper, Impact force, Vbraton & Depth and Wdth of cut Materal removal rate, Tool wear rate and Surface roughness Vol. 4 No. 6 June IJARIE 11

12 Fgure 1.Pe chart of publcaton year wth number of papers SERVICE 4 PARALLEL CUTTING MACHINE 1 ABRASIVE JET MACHINE 2 ECM OPEN-END SPUN YARNS 1 1 EDM 8 DRILLING 3 MILLING 1 LATHE CONCLUSION Fgure 2. Bar chart of machnes tools used n dfferent research papers From revewed dfferent research paper t has been concluded that Taguch grey relaton analyss s mostly used by dfferent researchers. Grey relaton analyss wdely used for optmzaton problem has mult objectves. The grey relaton analyss s not used for optmzaton of machnng process parameters but also used for call center ste selecton, bank selecton, vendor selecton etc. Grey Relaton analyss gves the sgnfcant mprovement from conventon results. Analyss of varance s also help to determne whch parameter shows the sgnfcant effect on requred performance characterstcs. Vol. 4 No. 6 June IJARIE 12

13 Internatonal Journal of Advanced Research n ISSN: Grey relaton analyss s the one of the smplest method and result gves sgnfcant mprovement from exstng solutons. REFERENCES 1. Abhjt Saha and N. K. Mandal, Optmzaton of machnng parameters of turnng operatons based on mult performance crtera, Internatonal Journal of Industral Engneerng Computatons 4, pp , Abhshek Dubey, Devendra Pathak, Nlesh Chandra, AjendraNath Mshra and Rahul Davs, A Parametrc Desgn Study of End Mllng Operaton usng Grey Based Taguch Method, Internatonal Journal of Emergng Technology and Advanced Engneerng, Volume 4, Issue 4, pp , Aprl Arun Kumar Parda, Rajesh Kumar Bhuyan and Bharat Chandra Routara, Multple characterstcs optmzaton n machnng of GFRP compostes usng Grey relatonal analyss, Internatonal Journal of Industral Engneerng Computatons 5, pp , B. Shvapragash, K. Chandrasekaran, C. Parthasarathy and M. Samuel, Mult Response Optmzatons n Drllng Usng Taguch and Grey Relatonal Analyss, Internatonal Journal of Modern Engneerng Research, Vol.3, Issue.2, pp , March-Aprl Chao-Leh Yang, Optmzng the Glass Fber Cuttng Process Usng the Taguch Methods and Grey Relatonal Analyss, New Journal of Glass and Ceramcs, 1, pp , Chh-Hung Tsa, Chng-Lang Chang and Leh Chen, Applyng Grey Relatonal Analyss to the Vendor Evaluaton Model, Internatonal Journal of The Computer, The Internet and Management, Vol. 11, No.3, pp , D. Chakradhar and A. Venu Gopal, Mult-Objectve Optmzaton of Electrochemcal machnng of EN31 steel by Grey Relatonal Analyss, Internatonal Journal of Modelng and Optmzaton, Vol. 1, No. 2, pp , June Funda OZCELIK & Burcu AVCI OZTURK, Evaluaton of Banks Sustanablty Performance n Turkey wth Grey Relatonal Analyss, The Journal of Accountng and Fnance, pp , July Vol. 4 No. 6 June IJARIE 13

14 9. Geeta Nagpal, Mon Uddn and Arvnder Kaur, Grey Relatonal Effort Analyss Technque Usng Regresson Methods for Software Estmaton, The Internatonal Arab Journal of Informaton Technology, Vol. 11, No. 5, pp , September Hossen Hasan, Somayeh Akhavan Tabatabae, Ghafour Amr, Grey Relatonal Analyss to Determne the Optmum Process Parameters for Open-End Spnnng Yarns, Journal of Engneered Fbers and Fabrcs, Volume 7, Issue 2, pp , J. T. Huang and Y. S. Lao, Applcaton of Grey Relatonal Analyss to Machnng Parameters Determnaton of Wre Electrcal Dscharge Machnng, pp Kamal Jangra, Sandeep Grover and Aman Aggarwal, Smultaneous optmzaton of materal removal rate and surface roughness for WEDM of WCCo composte usng grey relatonal analyss along wth Taguch method, Internatonal Journal of Industral Engneerng Computatons 2, pp , M. S. Reza, M. Hamd and M. A. Azmr, Optmzaton of EDM Injecton Flushng Type Control Parameters Usng Grey Relatonal Analyss On AISI 304 Stanless Steel Work pece Natonal Conference n Mechancal Engneerng Research and Postgraduate Student, 26-27, pp , May Meenu Gupta and Surnder Kumar, Mult-objectve optmzaton of cuttng parameters n turnng usng grey relatonal analyss, Internatonal Journal of Industral Engneerng Computatons 4, pp , Mehul A. Raval and Chrag P. Patel, Parametrc Optmzaton of Magnetc Abrasve Water Jet Machnng Of AISI Steel usng Grey Relatonal Analyss, Internatonal Journal of Engneerng Research and Applcatons, Vol. 3, Issue 4, pp , May-Jun 2013,. 16. P. Narender Sngh, K. Raghukandan & B.C. Pa, Optmzaton by Grey relatonal analyss of EDM parameters on machnng Al 10%SC compostes, Journal of Materals Processng Technology , pp , Pankaj Sharma, Kamaljeet Bhambr, Mult-Response Optmzaton By Expermental Investgaton of Machnng Parameters In CNC Turnng By Taguch Based Grey Relatonal Analyss, Internatonal Journal of Engneerng Research and Applcatons, Vol. 2, Issue 5, pp , September- October 2012,. Vol. 4 No. 6 June IJARIE 14

15 18. Raghuraman S, Thruppath K, Panneerselvam T & Santosh S, Optmzaton Of EDM Parameters Usng Taguch Method and Grey Relatonal Analyss for Mld Steel IS 2026, Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology, Vol. 2, Issue 7, pp , July Reddy Sreenvasulu and Dr. Ch. Srnvasa Rao, Applcaton of Gray Relatonal Analyss for Surface Roughness and Roundness Error n Drllng of Al 6061 Alloy, Internatonal Journal of Lean Thnkng Volume 3, Issue 2, pp , December S V Subrahmanyam and M. M. M. Sarcar, Evaluaton of Optmal Parameters for machnng wth wre cut EDM Usng Grey-Taguch Method, Internatonal Journal of Scentfc and Research Publcatons, Volume 3, Issue 3, pp. 1-9, March Semra BIRGUN, Cengz GUNGOR, A Mult-Crtera Call Center Ste Selecton by Herarchy Grey Relatonal Analyss, Journal of Aeronautcs and Space Technologes, Volume 7 Number 1, pp.45-52, January Shunmugesh K, Panneerselvam. K and Jospaul Thomas, Optmsng Drllng Parameters Of GFRP By Usng Grey Relatonal Analyss, Internatonal Journal of Research n Engneerng and Technology, Volume: 03 Issue: 06, pp , Jun T Muthuramalngam and B. Mohan, Taguch grey relaton based mult response optmzaton of electrcal process parameters n electrcal dscharge machne, Indan Journal of Engneerng & Materal Scence, Vol. 20, pp , December T V K Gupta, J Ramkumar, Puneet Tandon and N S Vyas, Applcaton Of Grey Relatonal Analyss for Geometrcal Characterstcs n Abrasve Water Jet Mlled Channels, 5th Internatonal & 26th All Inda Manufacturng Technology, Desgn and Research Conference (AIMTDR 2014) December 12, IIT Guwahat, Assam, Inda, pp , V. Chttaranjan Das, N. V. V. S. Sudheer, Optmzaton of Multple Performance Characterstcs of the Electrcal Dscharge Machnng Process on Metal Matrx Composte (Al/5%Tcp) usng Grey Relatonal Analyss", 5th Internatonal & 26th All Inda Manufacturng Technology, Desgn and Research Conference (AIMTDR 2014) December 12, IIT, pp , Vol. 4 No. 6 June IJARIE 15