MULTI OBJECTIVE OPTIMIZATION OF FLUX CORED ARC WELD PARAMETERS USING FUZZY BASED DESIRABILITY FUNCTION *

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1 IJST, Transactons of Mechancal Engneerng, Vol. 37, No. M2, pp Prnted n The Islamc Republc of Iran, 2013 Shraz Unversty MULTI OBJECTIVE OPTIMIZATION OF FLUX CORED ARC WELD PARAMETERS USING FUZZY BASED DESIRABILITY FUNCTION * M. SATHEESH 1** AND J. EDWIN RAJA DHAS 2 1 Dept. of Mechancal Engneerng, Noorul Islam Centre for Hgher Educaton, Kumaracol, Taml nadu, Inda Emal: satheeshudaya@gmal.com 2 Dept. of Automoble Engneerng, Noorul Islam Centre for Hgher Educaton, Kumaracol, Taml nadu, Inda Abstract In recent years much research has been conducted to study the varatons n weldng parameters and consumables on the mechancal propertes of steels to optmze weld ntegrty. The qualty of weld s a very mportant workng aspect for the manufacturng and constructon ndustres. In the present work, an attempt has been made to apply an effcent technque, fuzzy based desrablty method to solve correlated multple response optmzaton problems, n the feld of flux cored arc weldng. Ths approach converts the complex multple objectves nto a sngle fuzzy reasonng grade. Based on fuzzy reasonng grade, optmum levels of parameters (Weldng current, arc voltage and electrode stckout) were dentfed. Experments were performed based on Taguch method. Weld bead hardness and materal deposton rate are selected as qualty targets. Sgnfcant contrbutons of parameters are estmated usng Analyss of Varance (ANOVA). Confrmaton test s conducted and reported. It s found that the electrode stckout s the most sgnfcant controlled factor for the process accordng to the weghted fuzzy reasonng grade of the maxmum weld bead hardness and materal deposton rate. The proposed technque allows manufacturers to develop ntellgent manufacturng system to acheve the hghest level of automaton. Keywords ANOVA, Deposton rate, Desrablty, Flux cored arc weldng, fuzzy, orthogonal array 1. INTRODUCTION Flux Cored Arc Weldng (FCAW) process s a fully automated process, n whch the weldng electrode s a tubular wre that s contnuously fed to the weld area. The flux materals are n the core of the tube. The outer shell of the tube conducts the electrcty that forms the arc and then becomes the fller metal as t s consumed [1]. Recent studes ndcate that FCAW has a number of advantages over the common weldng technques such as manual metal arc weldng and gas metal arc weldng [2]. Ghazvnloo et al. [3] studed the effect of arc voltage, weldng current and weldng speed on fatgue lfe, and mpact energy and bead penetraton of AA6061 jonts by robotc metal nert gas weldng. Effect of FCAW parameters on weld wdth and tensle propertes of weld metal n low carbon steel were nvestgated [4]. FCAW n repar weld technque provdes better control over current and heat nput to carry out the temper bead repar. As a fully automatc process, FCAW has cost advantages over other commonly used processes [5]. Qualty of a weld jont s greatly nfluenced by weldng parameters [6, 7]. Important parameters of flux cored arc weldng are: weldng current, ntensty, voltage, speed of weldng, wre dameter, length of wre stckout, thckness and gas flow rate. These parameters have to be selected and precsely controlled n a judcous manner to acheve weld of desred qualty. Weld qualty depends on features of bead geometry, mechancal metallurgcal characterstcs of the weld metal and HAZ, and on weld chemstry [8]. The Receved by the edtors July 30, 2012; Accepted Aprl 8, Correspondng author

2 176 M. Satheesh and J. Edwn Raja Dhas problem faced by the weld operator s how to control the process nput parameters to obtan a good welded jont wth the requred bead geometry and weld qualty wth mnmal detrmental resdual stresses and dstorton. Hence these parameters should be selected to reach the desred target by the area of applcaton of the weldment. To do so, weld nput parameters should be chosen by the skll of the engneer or machne operator whch s a tme-consumng tral and error development effort. In order to overcome ths problem, varous optmzaton methods have emerged to defne the desred output varables through developng mathematcal models to establsh the relatonshp between the nput parameters and output varables. Toyofum et al. [9] and Tsa et al. [10] observed the optmzaton values of weldng condtons n spral ppes and process parameters n hardfacng. Tarng and Yang [11] appled Taguch method to the optmzaton of the submerged arc weldng process. Gunaraj and Murugan [12] appled Response Surface Methodology for predcton and optmzaton of weld bead qualty n submerged arc weldng of ppes by establshng mathematcal models. Curvlnear equatons [13], lnear regresson equatons [14], multple regresson analyss [15] and Taguch method [11] have been used to model SAW process. Sabbaghan et al. [16] successfully appled Taguch method to optmze the process condtons n the producton of Lpase. However, tradtonal Taguch technque cannot solve mult-objectve optmzaton problem effcently and effectvely. The most commonly used approach for mult objectve problem s to assgn weghts for every response. To overcome these lmtatons Taguch based grey relatonal analyss approach [17] was developed to handle uncertan systematc problem wth only partal known nformaton. Tarng et al. [18] appled grey based Taguch method for optmzaton submerged arc weldng process parameters n hardfacng. In Grey Taguch approach, all qualty features are assumed to be ndependent. But n actual case, the assumpton may devate. To overcome ths, nstead of Grey-Taguch, Pearson and Hotellng developed Prncpal Component Analyss (PCA). Bswas et al. [19] appled PCA n Taguch method to optmze the bead geometry of Submerged Arc Weld parameters. The ndvdual prorty weghts are requred to be assgned to dfferent responses. In practce, these responses may not be of equal mportance. Degree of sgnfcance of varous responses depends on applcaton area and functonal requrements of the product. For good jont strength, the weldment should have hgher hardness and maxmum deposton rate [20]. In general, weld hardness s of vtal mportance. Therefore, prorty weght of hardness s to be set more compared to deposton rate. Assgnment of response prorty weghts bascally depends on the judgment of the decson maker. Change n value of the prorty weghts yelds change n the value of aggregated qualty ndex. Moreover, the above-mentoned approaches are based on the assumpton that responses are uncorrelated. Interdependence of the responses has been assumed neglgble whle n practce any change n one response remarkably affects another response. Thus, judgment of prorty weghts n conjuncton wth assumpton of neglgble response correlaton may lead to vagueness n the soluton. To overcome these lmtatons, hybrd methods have been ntroduced by researchers. Taguch based Utlty theory optmzaton concept [21] has been appled to predct process parameters of Submerged Arc Weldng. Naveen Sat et al. [22] used Taguch method n combnaton wth desrablty functon to optmze machnng parameters of glass-fbre-renforced plastc. Genetc algorthm wth Taguch method was used to optmze parameters of submerged arc weldng n hardfacng process [23] and weld bead geometry n plasma transferred arc hard faced austentc stanless steel plates [24]. Recently, optmzaton of machnng parameters for the mllng operaton was carred usng partcle swarm optmzaton algorthm [25]. Katherasan et al. [26] optmzed the parameters of FCAW process for better bead geometres usng partcle swam optmzaton algorthm. Ankta Sngh et al. [27] nvestgated optmzaton of bead geometry of submerged arc weld usng fuzzy based desrablty functon approach. Ln et al. [28] appled the hybrd Taguch- Fuzzy logc method for optmzaton of electrcal dscharge machnng process parameters. Mostafa Jafaran et al. [29] used Fuzzy - Topss IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2 October 2013

3 Mult objectve optmzaton of flux cored ARC 177 method to select the weldng process at hgh pressure vessel manufacturng. Esme et al. [30] appled desgn of experments and neural networks for predcton of surface roughness n wre electrcal dscharge Machnng. In ths artcle, applcaton of desrablty approach combned wth fuzzy logc analyss s appled to optmze the multple qualty characterstcs of flux cored arc weldng parameters. a) Desrablty functon 2. FUZZY BASED DESIRABILITY FUNCTION METHODOLOGY Desrablty functon approach s a powerful tool for solvng the multple performance characterstcs optmzaton problems, where all objectves attan a defnte goal smultaneously. Desrablty between 0 and 1 represents the closeness of a response to ts deal value. If a response falls wthn the unacceptable ntervals, the desrablty s 0, and f a response falls wthn the deal ntervals or the response reaches ts deal value, the desrablty s 1. The am of ths approach s to convert a multple performance characterstcs optmzaton problem nto a sngle response optmzaton problem wth the objectve functon of overall desrablty. Then the overall desrablty functon s optmzed. The desrablty functon method was ntroduced by Harrngton [31], who used the exponental type transformaton of response value to ts desrablty. Km and Ln [32] presented a more general desrablty transformaton, whch s flexble to the analysts. Accordng to Derrnger and Such [33] approach, one- and two-sded desrablty functons are used dependng on whether the response s to be maxmzed or mnmzed or has an assgned target value. Let L and H be the lower and upper specfcaton lmts and T be the target value of the th response respectvely (such that L T H ). For a response y wth a target value (nomnal s best), the ndvdual desrablty s defned as Eq. (1). 0, y L s [( y L) /( T L)], L y T d (1) t [( H y) /( H T)], T y H 0, y H where the weghts s and t determne how strctly the target value s desred. If the response y s at ts goal or target, then d = 1, and f the response s outsde the acceptable regon, d = 0. The value of d ncreases as the "desrablty" of the correspondng response ncreases. Smlarly, one-sded desrablty functons for mnmzng or maxmzng case may be performed. For larger the- better problem, the ndvdual desrablty s gven by Eq. (2). 0, y L s d [( y L) /( T L)], L y T (2) 1, y T and for smaller-the-better problem, the ndvdual desrablty s calculated usng Eq. (3) 1, y T t d [( H y) /( H T)], T y H 0, y H The ndvdual desrablty values have been accumulated to calculate the overall desrablty usng Eq. (4). w 1/ 1 w2 W W n Do ( d1 d2... dn ) (4) (3) October 2013 IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2

4 178 M. Satheesh and J. Edwn Raja Dhas Here D o s the overall desrablty value, d s the ndvdual desrablty value of th qualty characterstc and n s the total number of responses. W s the weght for th attrbute. Sum of all attrbute weghts should be equal to 1. However, overall desrablty Do can be treated as equvalent aggregated qualty ndex but the problem arses n assgnng prorty weghts of varous responses. Lterature shows that prevous nvestgators determned optmal settng of process parameters [34] by maxmzng D o n the expermental doman. The results obtaned thereof may be naccurate because the exact value of prorty weght to be assgned to each ndvdual response s dffcult to predct. Therefore to sort-out these lmtatons, fuzzy logc approach combned wth desrablty functon has been ntroduced. The flow chart of the fuzzy logc controller coupled wth desrablty functon method used n the study s depcted n Fg. 1. b) Fuzzy logc analyss The theory of fuzzy logc was ntated by Zadeh [35]. It s a way of representng nformaton that mmcs human reasonng about nformaton [36]. The most nterestng fact about fuzzy logc s that fuzzy nferences make t possble to deduce a proposton smlar to the consequence from some propostons that are smlar to the antecedent [37]. Start Orthogonal array expermental desgn Experments propagaton Measurement of responses Desrablty functon values Fuzzfcaton Data base Fuzzy knowledge base Fuzzy nference engne Rule base Fuzzy logc controller Defuzzfcaton Determne Fuzzy Reasonng Grade (FRG) wth fuzzy nference system Get optmal control factor level wth effect plot Analyss of varance Proposed verfcaton experment Stop Fg. 1. Flowchart of fuzzy logc controller coupled wth Taguch method Fuzzy controllers and fuzzy reasonng [38] have found partcular applcatons n very complex ndustral systems that cannot be modeled precsely even under varous assumptons and approxmatons. IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2 October 2013

5 Mult objectve optmzaton of flux cored ARC 179 The fuzzy logc approach combned wth Taguch technques has been appled to optmze multple objectves n machnng process [39]. Fuzzy system s composed of a fuzzfer, an nference engne, a data base, a rule base, and defuzzfer. In the study, the fuzzfer ntally uses membershp functons to convert crsp nputs nto fuzzy sets. Once all crsp nput values have been fuzzfed nto ther respectve lngustc values, the nference engne wll access the fuzzy rule base of the fuzzy expert system to derve lngustc values for the ntermedate as well as the output lngustc varables. The fuzzy rule base conssts of a group of f-then control rules wth the two desrablty functon values, x 1 and x 2 one mult response output y, that s: Rule 1: f x 1 s A 1 and x 2 s B 1 then y s C 1 else Rule 2: f x 1 s A 2 and x 2 s B 2 then y s C 2 else Rule n: f x 1 s A n and x 2 s B n then y s C n. A, and B, are fuzzy subsets defned by the correspondng membershp functons,.e. A and B. Suppose x 1 and x 2 are the two desrablty values, the membershp functon of the mult-response output y s expressed n Eq. (5). E ( y) ( ( 1 1) ( 1 2) ( ))... o A x B x C y 1 (5) ( ( x ) ( x ) ( y)) An 1 B n 2 C1 Where and are the mnmum and maxmum operaton respectvely. Equaton (5) s llustrated n Fg. 2. Fg. 2. Mamdan mplcaton methods wth fuzzy controller operatons Fnally, a centrod defuzzfcaton method s adopted to transform the fuzzy mult-response output c ( y) o nto a non- fuzzy value y o, Eq. (6). yc ( y) o yo C ( y) o (6) 3. EXPERIMENTAL METHODS Experments were conducted usng SUPRA INVMIG 500 weldng machne by DC electrode postve power supply. Test peces of sze 200mm 150mm 6 mm were cut from low carbon structural steel (IS: 2062) plate and ts surfaces were ground to remove oxde scale and drt before weldng. Flux cored mld steel electrode (E71T-1) of 1.2 mm dameter was used for weldng. CO 2 gas at a constant flow rate of 15 L/mn was used for sheldng. The expermental setup used conssts of a travelng carrage wth a table for October 2013 IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2

6 180 M. Satheesh and J. Edwn Raja Dhas supportng the specmens. The weldng torch s held statonary n a frame mounted above the work table, and t was provded wth an attachment for both up and down movement and angular movement for settng the requred nozzle-to-plate dstance and weldng torch angle, respectvely. Sngle pass weldng bead on jont weld wth square butt weld s performed on the weld plates by varyng the ntal parameters as shown n Table 1. The workng ranges for the process parameters were selected from the Amercan Weldng Socety handbook [40]. Based on the desgned L 27 orthogonal array combnaton a seres of jonng processes s performed n weldng machne. Each tral of experment was done twce and the average value taken. The photograph of the expermental set up s shown n Fg. 3. Deposton rate and hardness are consdered as objectves. The metal deposton rate was calculated wth the help of stop watch and length of the electrode melt durng the weldng process. Hardness test was performed usng Brnnel Hardness testng machne. The expermental desgn and observed values from the specmens are gven n Table 2. Table 1. Process parameters and ther levels No Process parameters Level-1 Level-2 Level-3 1 Weldng current (I), ampere Arc voltage (V), volts Electrode stckout (S), mm a) Orthogonal array experment Fg. 3. Photographc vew of expermental setup 4. RESULTS AND DISCUSSION In the present study, the nteracton between the weldng parameters s neglected. Therefore, degrees of freedom due to the three sets and three level weldng process parameters are analyzed. The degrees of freedom for the orthogonal array should be greater than or at least equal to those for the process parameters. In ths study, an orthogonal array wth two columns and 27 rows s used. Ths array has 6 degrees of freedom and t can handle three-level process parameters. Expermental layout for the weldng process parameters usng the orthogonal array s shown n Table 2. b) Mult objectve optmzaton Snce the tradtonal Taguch method deals wth sngle response, t s necessary to convert two objectves nto sngle performance ndex. Therefore, desrablty values have been computed for the selected response parameters. In ths calculaton, lnear desrablty functon has been chosen (desrablty functon ndex unty). Whle calculatng varous desrablty values, a Hgher-the-better (HB) crteron has been selected for hardness and deposton rate. (Eq. (2)). These selectons are based on functonal IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2 October 2013

7 Mult objectve optmzaton of flux cored ARC 181 requrements of the weldment when subjected to applcaton feld. The calculated ndvdual desrablty values correspondng to each parameter are shown n Table 3. In order to calculate overall desrablty, a fuzzy nference system has been proposed to obtan ndvdual response desrablty values. These values have been treated as two nputs and Fuzzy Reasonng Grade (FRG) as output. The optmal process settng may be evaluated by maxmzng ths FRG. In ths study, the most popular defuzzfcaton method s the centrod calculaton, whch returns the centre of area under the curve. The defuzzfer converts the fuzzy value nto non-fuzzy value whch s called fuzzy reasonng grade. The MF adopted n ths s trapezodal MF, whch has a flat top and s really just a truncated trangle curve. There are fve fuzzy subsets assgned n the desrablty values for bead hardness and deposton rate: very small, small, mddle, large and very large as shown n Fg. 4. Nne fuzzy subsets are assgned n the mult-response output: tny, very small, small, small-medum, medum, medum-large, large, very large, and huge (Fg. 5). Table 2. Expermental results of Hardness and Deposton rate Ex.No. I V S Hardness (HB) Deposton rate (Kg/hr) Varous degrees of membershp of the fuzzy sets are calculated based on the values of x1, x 2 and y. Thus, straghtaway 25 fuzzy rules are derved based on the larger S/N rato beng the better process response. A fuzzy mult-response output s produced from these rules by takng the max mn nference operaton. October 2013 IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2

8 182 M. Satheesh and J. Edwn Raja Dhas Table 3. Indvdual desrablty values and fuzzy reasonng grade Ex. No Indvdual desrablty values Fuzzy reasonng Hardness Deposton rate Grade (FRG) Fg. 4. Membershp functons for desrablty functon Fg. 5. Membershp functons for desrablty-fuzzy reasonng grade IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2 October 2013

9 Mult objectve optmzaton of flux cored ARC 183 Based on the above dscusson, the larger the fuzzy reasonng grade, the better s the multple process responses. Table 3 shows the expermental results for the fuzzy reasonng grade. Thus, the mult-crtera optmzaton problem has been transformed nto a sngle objectve optmzaton problem usng the combnaton of desrablty functon fuzzy logc analyss. The sequence wth largest fuzzy reasonng grade ndcates t s the closest to the desred values of the qualty characterstcs. Fuzzy analyss procedure for the optmal condtons s graphcally presented n Fg. 6, n whch rows represent 25 rules and columns are the three nputs and one output varable. The locatons of trapezodal ndcates the determned fuzzy sets for each nput and output value. The renforcement of the darkened area n each trapezodal corresponds to the fuzzy membershp value for that fuzzy set. To determne the optmal process parameters, the effect of each weld process parameter on the rato at dfferent levels s separated out snce the expermental desgn s orthogonal. To obtan the effect of each control factor on each qualty characterstc for each level, the ratos wth same level of control factor are averaged for 27 experments. From Table 4, t s concluded that the parameter combnaton I 3 VS 2 2 has the best performance for all the qualty characterstcs. The response plot for the overall fuzzy reasonng grade s represented graphcally n Fg. 7. c) Analyss of varance Fg. 6. Fuzzy logc reasonng procedure for the results by the optmal Condtons ANOVA s performed to dentfy the process parameters of flux cored arc weldng that sgnfcantly affect the multple performance characterstcs. An ANOVA table conssts of sums of squares, correspondng degrees of freedom, the F-ratos correspondng to the ratos of two mean squares, and the contrbuton proportons from each of the control factors. These contrbuton proportons are used to assess the mportance of each factor for the nterested multple performance characterstc. The result of ANOVA for multple qualty characterstcs (Table 5) shows that electrode stckout s the most sgnfcant control factor followed by weldng current. The percentage contrbuton of each control factor to the total varance s electrode stckout %, weldng current % and arc voltage 13.32%. Here, arc voltage s found to be a less sgnfcant factor n nfluencng the overall fuzzy reasonng grade. October 2013 IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2

10 184 M. Satheesh and J. Edwn Raja Dhas Table 4. Response table for fuzzy reasonng grade Weldng Parameters Symbol Fuzzy reasonng grade Level-1 Level-2 Level-3 Max-Mn Current (Ampere) I Voltage (V) V Stckout (mm) S Total mean of the fuzzy reasonng grade= I1 I2 I3 V1 V2 V3 S1 S2 S3 Fg. 7. Response graph for fuzzy reasonng grade d) Confrmaton test Confrmaton test s a crucal step recommended by Taguch to verfy expermental concluson. The mprovements of the performance characterstc usng the optmal level of weld parameters are verfed. Estmated Grey relatonal grade (GRG) opt s calculated as q opt m ( m) (7) 1 Where m s total GRG;, mean GRG at optmum level; and q, number of process parameters havng sgnfcant contrbuton n multple performance characterstcs. Table 6 shows the comparson of the multple performance characterstcs for ntal and optmal weldng parameters. The ntal desgnated levels of weldng parameters are I 2, V 2 and S 2. As noted from Table 6, the bead hardness ncreases from to and deposton rate ncreases from 2.89 Kg/hr to 3.75 Kg/hr. The estmated fuzzy reasonng grade s ncreased from 0.551to It s observed that optmal desgn obtaned from the desrablty- fuzzy logc analyss has the largest experental and predcted fuzzy reasonng grade wth R 2 value of It s clearly shown that the multple objectves of the weld process mproved remarkably. Table 5. Results from ANOVA Factors Degrees of Sum of Mean Contrbuton F value P value Freedom Squares Squares % I V S Error Total Table 6. Results of weldng performance usng the ntal and optmal weldng factors Intal process Optmal process parameters parameters predcton experment Level I2V2S2 I3V2S2 I3V2S2 Hardness Deposton rate Fuzzy reasonng grade Improvement of Fuzzy reasonng grade IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2 October 2013

11 Mult objectve optmzaton of flux cored ARC CONCLUSION In the present work, experments are carred out to collect the data usng Taguch L 27 orthogonal array desgn. The hybrd, fuzzy-desrablty technque has been ntroduced to optmze the multple propertes of flux cored arc weldng. In ths method, snce the responses are lngustc n nature t s not essental to check nterdependence (correlaton) of the responses. Moreover, the ndvdual prorty weghts need not be assgned. Fuzzy nference system takes care of that. From ths analyss, t s revealed that electrode stckout and weldng current are predomnant factors whch affect the weld qualty of mld steel. The best performance characterstcs are obtaned wth an optmum parameter settng of I 3 VS 2 2. Confrmaton test proved that the determned optmum condton of weldng parameters satsfes the real requrement. It s found that the proposed procedure employed n ths study can resolve a complex parameter desgn problem wth multple responses. It could be appled to those areas where there are large data sets and a number of responses are to be optmzed smultaneously. REFERENCES 1. Wdgery, D. (1994). Tubular Wre Weldng. Woodhead Publ. Ltd. 2. Parmar, R. S. (1999). Weldng processes and technology, Khanna Publshers, New Delh. 3. Ghazvnloo, H. R., Honarbakhsh- Raouf, A. & Shadfar, N. (2010). Effect of arc voltage, weldng current and weldng speed on fatgue lfe, mpact energy and bead penetraton of AA6061 jonts produced by robotc MIG weldng. Indan J. Sc. Technol., Vol. 3, No. 2, pp Ghazvnloo, H. R. & Honarbakhsh-Raouf, A. (2010). Effect of Gas-flux cored arc weldng parameters on weld wdth and tensle propertes of weld metal n a low carbon steel. J. Appl. Sc, Murugan, N. & Parmar, R. S. (1994). Effects of MIG process parameters on the geometry of the bead n the automatc surfacng of stanless steel. Journal of Materal Processng Technology, Vol. 41, pp Houldcroft, P. T. (1989). Submerged-Arc Weldng. Woodhead Publshng Ltd. England. 7. Weldng Handbook. (1978). Amercan Weldng Socety. Vol Saurav Datta, Goutam Nand, Assh Bandyopadhyay, Pradp Kumar Pal. (2009). Applcaton of PCA-based hybrd Taguch method for correlated multcrtera optmzaton of submerged arc weld: a case study. Int J Adv Manuf Technol. Vol. 45, pp Toyofum, K., Hrotaka, N. W., Yuko, N. & Katsuyuk, S. (1986). Optmzaton of weldng materals and condtons for hgh speed submerged arc weldng of spral ppe. Transactons of the Iron and Steel Insttute of Japan. Vol. 26, No. 5, pp Tsa, H. L., Tarng, Y. S. & Tseng, C. M. (1996). Optmzaton of submerged arc weldng process parameters n hardfacng. Internatonal Journal of Advanced Manufacturng Technology. Vol. 12, pp Tarng, Y. S., Yang, W. H. (1998). Applcaton of the Taguch Method to the Optmzaton of the Submerged Arc Weldng Process. Materals and Manufacturng Processes. Vol. 13, No. 3, pp Gunaraj, V. & Murugan, N. (1999). Applcaton of response surface methodology for predctng weld bead qualty n submerged arc weldng of ppes. Journal of Materals Processng and Technology. Vol. 88, No.1, Yang, L. J. & Chandel, R. S. (1993). An analyss of curvlnear regresson equatons for modelng the submerged-arc weldng process. Journal of Materals Processng and Technology. Vol. 37, pp Yang, L. J., Bbby, M. J. & Chandel, R. S. (1993). Lnear regresson equatons for modelng the submerged arc welds. Journal of Materals Processng and Technology. Vol. 39, pp Lee, J. L. & Rhee, S. (2000). Predcton of process parameters for gas metal arc weldng by multple regresson analyss. Proceedngs of Insttuton of Mechancal Engneers B. Vol. 14, pp October 2013 IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2

12 186 M. Satheesh and J. Edwn Raja Dhas 16. Sabbaghan, E. S. & Roostaazad, R. (2005). Applcaton of the Taguch method to optmze the process condtons n the producton of lpase by pseudomonas Aerognosa B Iranan Journal of Scence & Technology, Transacton B, Engneerng, Vol. 29, pp Deng, J. (1989). Introducton to grey system. The Journal of Grey System. Vol. 1, pp Tarng, Y. S., Juang, S. C. & Chang, C. H. (2002). The use of grey based Taguch methods to determne submerged arc weldng process parameters n hardfacng. Journal of Materals Processng Technology. Vol. 128, pp Bswas, A., Bhaumk, S., Majumdar, G., Saurav Datta. & Mahapatra, S. S. (2011). Bead Geometry Optmzaton of Submerged Arc Weld: Exploraton of Weghted Prncpal Component Analyss (WPCA). Appled Mechancs and Materals. Vol. 110, pp Chandel, S., Seow, H. P. & Cheong, F. L. (1997). Effect of ncreasng deposton rate on the bead geometry of submerged arc welds. Journal of Materals Processng Technology, Vol. 72, pp John Deb Barma, S. C., Joydeep Roy, Saha, B. (2012). Process Parametrc Optmzaton of Submerged Arc Weldng by Usng Utlty Based Taguch Concept. Advanced Materals Research. Vol. 1194, pp Naveen Sat, A., Aravndan, S. & Noorul Haq, A. (2009). Optmsaton of machnng parameters of glass-fbrerenforced plastc (GFRP) ppes by desrablty functon analyss usng Taguch technque. Int J Adv Manuf Technol. Vol. 43, pp Amar Patnak, S. S., Bswas, S. & Mahapatra. (2007). An evolutonary approach to parameter optmzaton of submerged arc weldng n the hard facng process. Int. J. Manuf. Res. Vol. 2, pp Sva, K., Murugan, N. & Logesh, R. (2009). Optmzaton of weld bead geometry n plasma transferred arc hardfaced austentc stanless steel plates usng genetc algorthm. Int. J. Adv. Manuf. Technol. Vol. 41, pp Baskar, N., Asokan, P., Saravanan, R. & Prabhaharan, G. (2005). Optmzaton of machnng parameters for mllng operatons usng non-conventonal methods. Int. J. Adv. Manuf. Technol, Vol. 25, pp Katherasan, D., Jju, V., Elas, P., Sathya, A. & Noorul Haq. (2012). Flux Cored Arc Weldng Parameter Optmzaton Usng Partcle Swarm Optmzaton Algorthm. Proceda Engneerng, Vol. 38, pp Ankta Sngh, Saurav Datta, Sba Sankar Mahapatra, Tapan Sngha & Gautam Majumdar. (2011) Optmzaton of bead geometry of submerged arc weld usng fuzzy based desrablty functon approach. J Intell Manuf. DOI /s Ln, C. L., Ln, J. L. & Ko, T. C. (2002). Optmsaton of the EDM Process Based on the Orthogonal Array wth Fuzzy Logc and Grey Relatonal Analyss Method. Int J Adv Manuf Technol. Vol. 19, pp Mostafa Jafaran, S. & Ebrahm Vahdat. (2012). A fuzzy mult-attrbute approach to select the weldng process at hgh pressure vessel manufacturng. Journal of Manufacturng Processes, Vol.14, No. 3, pp Esme, U. Sagbas, A. & Kahraman, F. (2009). Predcton of surface roughness n wre electrcal dscharge machnng usng desgn of experments and neural networks. Iranan Journal of Scence & Technology, Transacton B, Engneerng, Vol. 33, pp Harrngton, E. C. (1965). The desrablty functon. Industral Qualty Control. Vol. 21, pp Km, K. J. & Ln, D. K. J. (2000). Smultaneous optmzaton of mechancal propertes of steel by polynomal regresson functons. Appl. Stat. Vol. 49, pp Derrnger, G. & Such, R. (1980). Smultaneous optmzaton of several response varables. Journal of Qualty Technology, Vol. 12, pp Datta, S., Bandyopadhyay, A. & Pal, P. K. (2006). Desrablty functon approach for solvng mult-objectve optmzaton problem n submerged arc weldng. Internatonal Journal for Manufacturng Scence and Producton. Vol. 7, No. 2, Zadeh, L. (1965). Fuzzy sets Informaton Control. Vol. 8, pp IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2 October 2013

13 Mult objectve optmzaton of flux cored ARC Zmmermann, H. J. (1985). Applcatons of fuzzy sets theory to mathematcal programmng. Informaton Scences. Vol. 36, pp Dder Dubos & Henr Prade. (1982). Fuzzy sets and systems: Theory and applcatons. Amercan Mathematcal socety. Vol. 7, No.3, pp Chang, K. T., Lu, N. M. & Chou, C. C. (2008). Machnng parameter optmzaton on the de castng process of magnesum alloy usng the grey-based fuzzy algorthm. Internatonal Journal of Advanced Manufacturng Technology. Vol. 38, pp Ahlan, C., Kumanan, S. & Svakumaran, N. (2009). Mult objectve optmzaton of CNC turnng process usng grey based fuzzy logc. Internatonal Journal of Machnng and Machnablty of materals. Vol. 5, No.4, pp Weldng Handbook. (1978). Amercan Weldng Socety, Vol. 2. October 2013 IJST, Transactons of Mechancal Engneerng, Volume 37, Number M2