ANN MODELING AND MULTI OBJECTIVE OPTIMIZATION OF ELECTRICAL DISCHARGE MACHINING PROCESS

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1 ELK Asa Pacfc Journals Specal Issue ANN MODELING AND MULTI OBJECTIVE OPTIMIZATION OF ELECTRICAL DISCHARGE MACHINING PROCESS Sanjeev Kumar Sngh Yadav Assstant Professor, MED,HBTI Kanpur, UP, Inda Deepak Azad M.Tech Scholar, HBTI Kanpur, UP, Inda ABSTRACT Ths paper presents the modelng and optmzaton of EDM process for materal removal rate and average surface roughness, durng machnng of nckel alloy materal. The experments were carred out on ZNC EDM, durng the machnng three nput parameters were selected such as pulse current (I), pulse on- tme (Ton) and duty (Df) n whch each parameter was at three levels. The output parameters such as metal removal rate (MRR) and surface roughness (Ra) were selected for the work. In the present work the artfcal neural network (ANN) has been developed for modelng and grey relatonal analyss (GRA) has been used for process optmzaton. The total degree of freedom (DOF) has been calculated wthout consderng the effect of nteracton among the dfferent control. Based on DOM L 9 orthogonal array was selected for the process. The expermental data set were grouped nto tranng data and testng data. The developed ANN archtecture of has been found to predct MRR and Ra. After optmzaton of the process parameters optmum parameters were determned as A3 (pulse current, 12A), B3 (pulse on tme, 120 μs) and C2 (duty, 0.857). KEYWORDS- Current, ANN, GRA, INTRODUCTION Among all practcally applcable Advanced Machnng Processes (AMPs), EDM has attracted much research attenton due to ts broad ndustral applcatons. EDM s extensvely used n ndustry for machnng dffcult-to-machne electrcally conductve materals wth complex shapes whch are wdely used n tool and de manufacturng. At present, EDM s a wdely accepted machnng technque used for machnng all type of electrcally conductve materals ncludng metals and alloys, conductve ceramcs and compostes. The Present paper descrbes processng and optmzaton of EDM process for N-alloy workpece materal. Nckel alloys and nckel-based super alloys are known for superor resstance to heat and corroson as well as low thermal expanson propertes. These propertes make t useful n ndustres that requre parts to retan stablty as well as the ablty to resst corroson over a wde range of temperatures. Nckel Alloy extensvely used n power generaton ndustry and aerospace. In ths process sparkng takes place between two electrodes a small amount of materal s removed from each of the electrodes. Ths fact was realzed and the attempts were made to hardness and control the spark energy to employ t for useful purpose, say, for machnng of metals. Whle machnng spark s produced between the two electrodes (tool and workpece) and ts locaton s determned by the narrowest gap between the two. Duraton of each spark s very short. The entre cycle tme s usually few mcro-second. The frequency of sparkng may be as hgh as thousands of sparks per second. The area over whch a spark s effectve (or spark radus) s also very small. However, temperature of the area under the spark s very hgh. As a result, the spark energy s capable of partly meltng and partly vaporzng materal from localzed area on both the electrodes,.e. workpece and tool. The materal s removed n the form of craters whch spread over the entre surface of the workpece [1]. Fnally, the cavty produced n the workpece s approxmately the replca of the tool. To have machned cavty as replca of the tool, the tool wear should be zero. To mnmze wear of the tool the operatng parameters and polarty should be selected carefully. Partcles eroded from the electrodes are known as debrs. Usually the amount of materal eroded from the tool surface s much smaller than that from the workpece surface. In order to mprove producton rate and qualty of machned part due to EDM process, t s necessary to select the most preferred machnng condtons. It s dffcult to get hghest producton rate due to complex EDM process because of ther too many adjustable machnng parameters. Two EDM process responses

2 ELK Asa Pacfc Journals Specal Issue namely MRR for producton rate of product and R a for qualty of machned surface are prefer for study. However, the goal of achevng hgh MRR and low R a are conflctng n nature. Consequently, no partcular combnaton of machnng parameters can be proposed to gve smultaneously the hgh MRR and low R a. Many optmzaton technques have been used by researchers for the determnaton of preferred nput condtons for optmum value of output parameters. Mahadavnejad [2] conducted expermentaton on slcon carbde (SC) workpece materal and taken copper as a tool materal. They have optmzed the surface roughness and materal removal rate of electro dscharge machnng parameters smultaneously. Durng expermentaton they have taken current, pulse on tme, and pulse off tme as a nput. Further, artfcal neural network (ANN) wth back propagaton algorthm was used to model the process. A mult-objectve optmzaton method, nondomnatng sortng genetc algorthm-ii s used to optmze the process. Raghuraman et al. [3] have performed machnng on Mld steel IS 2026 usng copper electrode and nvestgated the optmal set of process parameters such as current, pulse on tme and off tme n Electrcal Dscharge Machnng (EDM) process to dentfy the varatons n three performance characterstcs such as rate of materal removal, wear rate on tool, and surface roughness value on the work materal. Grey relatonal analyss was used to optmze parameter. They have shown that the machnng parameters for EDM were optmzed for achevng the combned objectves of hgher rate of materal removal, lower wear rate on tool, and lower surface roughness value on the work materal consdered n ths work. Chakradhar et al. [4] performed experment on ECM machne. In ths EN31 steel whch s a hgh carbon steel used as workpece materal and electrolytc copper used as electrode. In ths paper the nput parameter are taken as electrolyte concentraton, feed rate and appled voltage and output parameter materal removal rate (MRR), overcut, cylndrcal error and surface roughness (Ra). The Grey relatonal analyss was used to optmzaton the process. They have targeted performance characterstcs,.e. materal removal rate can be maxmzed and the overcut, cylndrcal error and surface roughness can be mnmzed through ths method. EXPERIMENTAL WORK In the present research work all experments are conducted on ZNC electrc dscharge machne, S 50 ZNC (de- snkng type) wth servo-head (constant gap). The postve polarty was used for conduct the experments. Durng expermentaton commercal grade EDM ol (specfc gravty) = ( freezng pont= 94 C) was used as delectrc flud. Durng the present experment copper selected as tool materal for machnng. As Nckel alloy s a one of the super alloy due to broad applcaton n advanced manufacturng ndustres t s selected for the present work. FIGURE 1: PHOTOGRAPH OF ZNC EDM The sze of N- alloy workpece materal s taken as 15x15x8 mm. The propertes of N-alloy workpece materal are shown n Table 1 and Propertes Value Densty gm/cm 3 Meltng pont Bolng pont Thermal conductvty c c 90.7 w/m 0 c Hardness 200 BHN photograph of de snkng ZNC EDM s shown n Fg.1. TABLE 1: PROPERTIES OF WORK MATERIAL Based on degree of freedom the L 9 orthogonal array were selected. Two repettons for each expermentaton was also performed. Before expermentaton the selecton of range of nput parameters through plot expermentaton have been performed. After plot expermentaton the selected nput parameter and there levels are shown n Table 2. TABLE 2: INPUT PARAMETERS AND THEIR LEVELS

3 ELK Asa Pacfc Journals Specal Issue Input parameters Level 1 Level 2 Level 3 Current on-tme After selecton of nput parameters ranges the expermentaton were performed on selected levels and results are tabulated n Table 3. TABLE 3: RESPONSE TABLE FOR L9 ARRAY Ex No. MR R mm 3 /mn Ra µm MRR1 Ra1 mm µm 3 /m n MRR 2 mm 3 /m n Ra2 µm MODILLING Modelng s the scentfc way to study the process behavor whch helps to get the better understandng of any complex process. In the present problem MATLAB toolbox used to perform ANN modelng. The mult-layer feed forward ANN consst of three parts: nput layer, hdden layer, and the output layer were selected. The neurons between the layers are connected by the lnks havng synaptc weghts. The levenberg-marquardt back propagaton algorthm s used to mnmze mean square error. Frst, Mcrosoft excel worksheet used wth total of 27 nput and output expermental data and then mported the nput, output and sample data n the workspace. Further, network create wth varyng neuron n hdden layer and traned the network after that performance was checked and network selected when mean square error was mnmum. After approprate tranng the network found best. After tranng the data sample data s used to predct the output data. And fnally predcted output data are obtaned. The data used for selecton of traned neural network s shown n Table 4. NEURAL NETWORK TOPOLOGY, MEAN SQUARE ERROR AND CORRELATION COEFFICIENTS The best process parameter settng for ANN modelng was selected wth the help of full al method. The chosen optmal process parameters are Levenberg-Marquardt tranng algorthm, 16 nos. of hdden neurons MATLAB representaton of ANN topology that has been utlzed for modelng. Varaton of MSE of tranng, valdaton and testng data set w.r.t. the epoch has been shown n Fg 2. Valdaton data set s used to stop the tranng process n early stoppng crtera for provdng better generalzaton. Fg 2 shows that the valdaton error s mnmum at epoch 5. So the tranng was stopped at ths pont the weghts and bases were used to model MRR and Ra. Weghts and bases used for generatng the ANN outputs whch was be further used to do optmzaton process. TABLE 4: TRAINING DATA SET Exp. No. (A) on tme (µs) MRR mm 3 /mn Ra (µm)

4 ELK Asa Pacfc Journals Specal Issue FIGURE 2- VARIATION OF MSE W.R.T. EPOCHS FIGURE 3-CORRELATION COEFFICIENTS TABLE 5-ANN PREDICTED TABLE Exp. on MRR No. current tme (mm /mn) 3 (A) (µs) Ra (µm) OPTIMIZATION Optmzaton refers to fndng one or more feasble solutons whch correspond to extreme values of one or more objectves. Because of such extreme values of optmal soluton, optmzaton methods are of great mportance n practce, partcularly n engneerng desgn and scentfc experments. When an optmzaton problem modelng a physcal system nvolves only one objectve functon, the task of fndng the optmal soluton s called sngle-objectve optmzaton. Whereas when an optmzaton problem nvolves more than one objectve functon, the task of fndng one or more optmum soluton s known as mult-objectve optmzaton (MOO). Constrants optmzaton s mportant n practce, snce most realworld optmzaton problems nvolve constrants restrctng some propertes of the system to le wthn pre-specfed lmts. Convergence proofs and specaldynamc programmng, geometrc programmng, stochastc programmng, and varous others. Not enough emphass s usually gven to mult-objectve optmzaton. In MOO, a number of optmal solutons arsng because of trade-off between conflctng objectves are mportant. GRA s well acceptable optmzaton method for advanced machnng. In GRA, black represent havng no nformaton and whte represents havng all nformaton. A grey system has a level of nformaton between black and whte. When the range of sequences s large or the standard value s large, the functon of s s neglected. However, f the s measured unt, goals and drectons are dfferent, the grey relatonal analyss mght produce ncorrect results. Therefore, orgnal expermental data must be pre-processed to avod such effects. Data pre-processng s the process of transformng the orgnal sequence to a comparable sequence. For ths purpose, the expermental results are normalzed n the range of zero and one, the process s called grey relatonal generatng. STEP 1-Three dfferent types of data normalzaton accordng to whether we requre the Lower the better (LB), the hgher the better (HB), and Nomnal the better (NB).The normalzaton s taken by the followng equatons. Lower the better (LB) X * max X X max X mn X Hgher the better (HB)

5 ELK Asa Pacfc Journals Specal Issue X * X mn X max X mn X lower-the-better crteron can be expressed as- Zj = Nomnal the better (NB) X * X X d 1 max X X d Where =1,2,,m; k =1,2,,n; X * ( k ): the normalzed value of the k th element n the th sequence, (k) : the desred value of the k th qualty X d characterstcs, max X s the largest value of X( k ),and mn X( k ) s the smallest value of X( k ), m s the number of experments and n s the number of qualty characterstcs. TABLE 6: SIGNAL-TO-NOISE RATIO Exp. Predcted S/N rato No. value MRR Ra (µm) MRR Ra (µm) 1 (mm 3 /m n) (mm 3 /m n) STEP 2: - In the 2nd step of the grey relatonal analyss, pre-processng of the data was frst performed for normalzng the raw data for analyss. Yj s normalzed as Zj (0 Zj 1) by the followngformula to avod the effect of adoptng dfferent unts and to reduce the varablty. The normalzed output parameter correspondng to the larger-the-better crteron can be expressed as TABLE 7: NORMALIZED SIGNAL-TO-NOISE RATIO Exp. No. current (A) on tme (µs) Normalzed S/N rato MRR Ra (mm 3 /mn) (µm) STEP 3: A grey relatonal coeffcent s calculated to dsplay the relatonshp between the optmal and actual normalzed expermental results. The grey relatonal coeffcent can be expressed as r 0, mn max, 1,..., m; max 0, k 1,..., n Where r0, ( k ) s the relatve dfference of k th element between comparatve sequence X and the reference sequence X 0 (also called as grey relatonal coeffcent), Δ 0,(k) s the absolute value of dfference between X 0(k) and X (k), X X * * 0 0 max max max X X k * * 0 mn mn mn X X k * * 0 ζ s a dstngushng or dentfcaton coeffcent, and ts value le between zero and one. In general, t s set to 0.5. Zj = Then for the output parameters, whch follow the

6 ELK Asa Pacfc Journals Specal Issue TABLE 8: DEVIATION SEQUENCES current (A) Devaton sequences On tme (µs) MRR 3 (mm /mn) Ra (µm) TABLE 9: GREY RELATIONAL COEFFICIENT Ex p. Puls e Puls e on Grey relaton No curr tme al MRR. e (µs) (mm 3 Ra (µm) / nt (A) mn ) Step 4: The grey relatonal grade was determned by averagng the grey relatonal coeffcent correspondng to each performance characterstc. The overall performance characterstc of the multple response process depends on the calculated grey relatonal grade. The grey relatonal grade can be TABLE 9: GREY RELATIONAL GRADE Ex p. No. curre nt (A) P ul se on Dut y fact or Grey relato nal grade Rank 1 1 tme (µs) Step 5: Determnaton of the Optmal Factor and ts level combnaton. The expermental desgn s orthogonal, t s possble to separate out the effect of each machnng parameter on the grey relatonal grade at dfferent levels. For example, the mean of the grey relatonal grade for the current at levels 1, 2 and 3 can be calculated by averagng the grey relatonal grade for the experments 1 to 3, 4 to 6, and 7 to 9 respectvely. The mean of the grey relatonal grade for each level of the machnng parameters s summarzed. 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. expressed as = Where, s the grey relatonal grade for the j th experment and k s the number of performance characterstcs. FIGURE 4: GREY RELATIONAL GRADE FOR MAXIMUM MRR AND MINIMUM Ra

7 ELK Asa Pacfc Journals Specal Issue 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. The optmal parameter combnaton was determned as A3 (pulse current, 12A), B3 (pulse on tme, 120μs) and C2 (duty, 0.857) whch s shown n Table 10. TABLE 10: THE MAIN EFFECTS OF THE FACTORS ON THE GREY RELATIONAL GRADE Parameter Level 1 Level 2 Level 3 Max- Mn Rank current -ontme FIGURE 5: GREY RELATIONAL GRADE FOR EACH LEVEL OF PARAMETER CONFIRMING RESULTS The confrmaton tests for the optmal parameters wth ts levels were conducted to evaluate qualty characterstcs for EDM of nckel alloy C-263. Table 5.9 shows hghest grey relatonal grade, ndcatng the ntal process parameter set of A3B3C2 for the best multple performance characterstcs among the nne experments. Table 11 shows the comparson of the

8 ELK Asa Pacfc Journals Specal Issue expermental results for the optmal condtons (A3B3C2) wth predcted results for optmal (A3B3C2) EDM parameters. TABLE 11: CONFIRMATION RESULTS Optmal process parameters Experment Predcted Level A3B3C2 A3B3C2 MRR (mm 3 /mn) Ra (µm) CONCLUSIONS Modelng and optmzaton of EDM process are very useful n academc as well as ndustres. In ths work our nvestgaton s machnng of nckel alloy C-263 workpece materal usng copper tool and then modelng and optmzaton are done. There are followng conclusons have been made 1. The expermental results confrmed the valdty of the used ANN for modelng and GRA for enhancng the machnng performance and optmzng the parameters n EDM. 2. The expermental as well as predcted value at optmum level are nearly equal to each other and therefore confrm the success of experment. 3. In the experment t was found that MRR and Ra ncreases wth an ncrease n current, pulse on tme and duty. 4. There are varous ANN archtecture have been studed, and the archtecture was found to be best archtecture. 5. Grey Relatonal Analyss assocated wth taguch were appled n ths work to mprove the mult-response characterstcs such as MRR and Surface Roughness for nckel alloy workpece materal durng EDM process. 6. The optmal parameters combnaton was found as A3B3C2.e. current at 12A, pulse on tme at 120μs and duty at REFERENCES [1] Jan V.K (2002) Advanced Machnng Processes Alled Publshers Pvt. Ltd. New Delh, ISBN [2] Mahdav Nejad R.A(2011) Modelng and Optmzaton of Electrcal Dscharge Machnng of SC Parameters, Usng Neural Network and Nondomnatng Sortng Genetc Algorthm (NSGA II) Materals Scences and Applcatons.2, [3] Raghuraman S, Thruppath K, Panneerselvam T, Santosh S (2013) optmzaton of EDM parameters usng taguch method and grey relatonal analyss for mld steel s 2026 Internatonal Journal ofinnovatve Research n Scence, Engneerng and Technology,Vol. 2, Issue 7, ISSN: [4] Chakradhar D and Gopal A. V (2011) Multobjectve optmzaton of electrochemcal machnng of EN31 steel by grey relatonal analyss Internatonal Journal of modellng and optmzaton, vol. 1 no 2. [5] Balasubramanan S and Ganapathy S (2011) Grey Relatonal Analyss to determne optmum process parameters for Wre Electro Dscharge Machnng (WEDM) Internatonal Journal of Engneerng Scence and Technology Vol. 3 No. 1 ISSN: [6] Rajyalakshm G and Ramaah P. V (2012) Smulaton, modellng and optmzaton of process parameters of wre EDM usng taguch-grey relatonal analyss Internatonal Journal of Advanced and nnovatve research ISSN: [7] ShandlyaP and TwarA (2014) Artfcal Neural Network Modelng and Optmzaton usng Genetc Algorthm of Machnng Process Journal of Automaton and Control Engneerng Vol. 2, No. 4. [8] Mandal D, Pal S. K and Saha P (2007) Modelng of electrcal dscharge machnng process usng back propagaton neural network and mult-objectve optmzaton usng non-domnatng sortng genetc algorthm-ii Journal of Materals Processng Technology [9] Assarzadeh S, and Ghoresh M (2008) Neuralnetwork-based modelng and optmzaton of the

9 ELK Asa Pacfc Journals Specal Issue electro-dscharge machnng process Internatonal Journal of Advanced Manufacturng Technology 39: [10] Raghav G, Kadam B.S, Kumar M (2013) Optmzaton of Materal Removal Rate n Electrc Dscharge Machnng Usng Mld Steel Internatonal Journal of Emergng Scence and Engneerng Volume-1, Issue-7 ISSN: