(IP), II. EXPERIMENTAL SET-UP

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1 Applcaton of Response Surface Methodology For Determnng MRR and TWR Model In De Snkng EDM of AISI 1045 Steel M. B. Patel* P. K. Patel* J. B. Patel* Prof. B. B. Patel** *(Fnal Year Under Graduate student, Sankalchand Patel College of Engneerng, Vsnagar) ** (Department of Mechancal Engneerng, Sankalchand Patel College of Engneerng, Vsnagar ) ABSTRACT: Whereas the effcency of tradtonal cuttng processes s lmted by the mechancal propertes of the processed materal and the complexty of the workpece geometry, electrcal dscharge machnng (EDM) beng a thermal eroson process, s subject to no such constrants. The base materal used for ths study was an AISI 1045 steel wth copper electrode. Ths study hghlghts the development of a comprehensve mathematcal model of Tool wear rato (TWR) and Materal removal rate (MRR) for correlatng the nteractve and hgher order nfluences of varous electrcal dscharge machnng parameters lke Peak current (IP), Spark on tme (Ton) and Spark off tme (Toff) through response surface methodology (RSM), utlzng relevant expermental data as obtaned through expermentaton. The machnng experments were conducted based on sequental approach usng Full Factoral. The adequacy of the above the proposed models have been tested through the analyss of varance (ANOVA). I. 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 the electrode, and the part beng machned n the presence of a delectrc flud. At present, EDM s a wdespread technque used n ndustry for hghprecson machnng of all types of conductve materals [1]. The workng prncple s based on the thermo electrc energy. The thermo electrc energy (n form of spark) s created between a workpece and an electrode submerged n a delectrc flud wth conducton of electrc current. The workpece and the electrode are separated by a specfc small gap, the so called spark gap, and pulsed dscharges occur n ths gap flled wth an nsulatng medum []. Mohan Kumar Pradhan et al. [3] descrbed by analyss of varance results reveal that Ip s the most nfluencng factor for MRR and G, havng the hghest degree of contrbutons of 87.61% and 81.90%, respectvely. In case of TWR, Ton has the hghest degree of contrbuton of 46.05% and s the most sgnfcant factor. Sameh S. Habb [4] hghlghts the development of comprehensve mathematcal model for correlatng the nteractve and hgher order nfluences of varous electrcal dscharge parameters lke MRR, TWR and SR through response surface methodology (RSM). C.H. Che Heron, B. Md. Deros, A. Gntng and M. Fauzah [5] were used copper electrodes wth dameter of 9.5,1 and 0 mm n EDM of AISI 1045 steel at two current settng of 3.5 and 6.5 A wth objectve of determnng possble correlaton between the EDM parameter and the machnablty factors (MRR &TWR). II. EXPERIMENTAL SET-UP A number of experments were conducted to study the effects of varous machnng parameters on EDM process. These studes were undertaken to nvestgate the effects of varous machnng parameters on Tool wear rato and Materal removal rate. The selected workpece materal for the research work s AISI 1045 steel was selected due to ts emergent range of applcatons n the feld of mould ndustres. Experments were conducted on JOEMARS Z 50 JM-3 de snkng machne usng postve polarty. The flushng pressure was 0.5 Kg/cm. The copper wth a dameter of 15 mm was used as a tool electrode and De-electrc flud-9 (DEF-9) was used as de electrc flud. The test condtons are depcted n Table-1. The materal MRR s expressed as the rato of the dfference of weght of the workpece before and after machnng to the machnng tme and densty of the materal as shown n eq. (1). W tb Wta MRR (1) D t Where, W tb weght before machnng of w/p (gm), W ta weght after machnng of w/p (gm), D densty of work-pece materal (gm/mm 3 ) & t tme consumed for machnng (mn) TWR s expressed as the volumetrc loss of tool per unt tme, expressed as W tb Wta TWR () D t Where, 17 P a g e

2 W tb weght before machnng of tool (gm), W ta Table : Expermental Responses weght after machnng of tool (gm), D densty of Materal removal tool materal (gm/mm 3 ) & t tme consumed for Tool wear rate Experment rate machnng (mn) (TWR) no. (MRR) (mm 3 (mm /mn.) 3 /mn.) The 3 q factoral desgn s a factoral arrangement wth q factors, each at three levels. The levels of factor refer to as low, ntermedate, and hgh, represented by the dgt 1 (low), (ntermedate),and 3 (hgh). For nstance, n a 3 3 desgn, 1-3- ndcates the treatment combnaton correspondng to factor A at the low level, B at the hgh level, and C at the ntermedate level. When the measurements on the response varable contan all possble combnatons of the levels of the factors, ths type of expermental desgn s called a complete factoral experment. Accordng to 3 3 full factoral desgn we choose varous parameter and ther levels as shown n the below table for our expermentaton. Table 1: Codng levels of process parameters Levels Parameter 1 3 Current (A) Pulse on tme (µs) (B) Pulse of Tme (µs) (C) III. RESPONSE SURFACE METHODOLOGY RSM s a collecton of mathematcal and statstcal technques that are useful for modelng and analyss of problems n whch the response of nterest s nfluenced by several varables and objectve s to optmze ths response [6, 7]. In order to study the effects of the EDM parameters on the above mentoned machnng crtera, second order polynomal response surface mathematcal models can be developed. In the general case, the response surface s descrbed by an equaton of the form: Y k k 0 x x j x x j r 1 1 j Where Y s the correspondng response, (3) X s the nput varables, X and X X are the squares and j nteracton terms, respectvely, of these nput varables. The unknown regresson coeffcents are 0,, j and. Usng Full factoral method varous 7 number of experments to be conducted as shown n Table: IV. REGRESSION MODELS Based on the expermental data gathered, statstcal regresson analyss enabled to study the correlaton of process parameters wth the MRR and TWR. EFFECT OF PROCESS VARIABLES ON MRR AND TWR: The regresson coeffcents of the second order equaton are obtaned by usng the expermental data (Table 3, 5). The regresson equaton for the MRR and TWR as a functon of three nput process varables were developed usng expermental data and s gven below eq. (4, 5). The model adequacy checkng ncludes the test for sgnfcance of the regresson model, model coeffcents, and lack of ft, whch s carred out 18 P a g e

3 subsequently usng ANOVA on the curtaled model for MRR and TWR (Table-4, 6). Table 3: Estmated Regresson Coeffcents for MRR Term Coef SE Coef T P Constant Ip Ton Toff Ip*Ip Ton*Ton Toff*Toff Ip*Ton Ip*Toff Ton*Toff R-Sq = 99.70% R-Sq(pred) = 99.35% R-Sq(adj) = 99.55% MRR E E 03Ton E 01Toff E 7.54E 03TonTon 3.88E 04Toff Toff E Ton 3.640E Toff 7.143E 03TonToff (4) Table 4: Analyss of Varance for MRR Source DF Seq SS Adj SS Adj MS F P Regresson Lnear Square Interacton Resdual Error Total Table 5: Estmated Regresson Coeffcents for TWR Term Coef SE Coef T P Constant Ip Ton Toff Ip*Ip Ton*Ton Toff*Toff Ip*Ton Ip*Toff Ton*Toff R-Sq = 8.69% R-Sq(adj) = 7.96% Table 6: Analyss of Varance for TWR Source DF Seq SS Adj SS Adj MS F P Regresson Lnear Square Interacton Resdual Error Total P a g e

4 TWR E Ip.9539E Ton E Toff 3.89E 03 Ip 6.58E 04TonTon 1.50E 04Toff Toff.7E 03 Ton 1.51E 03 Toff 4.37E 04TonToff (5) Maxmum MRR s obtaned at hgh peak current and low pulse off tme. Fgure 3 represents MRR as a functon of Ton and Toff, whereas the Ip remans constant at ts mddle level. It s observed that the MRR values are low when Ton s low wth hgher Toff. From the analyss t s sad that the nteracton of Ton and Toff s sgnfcant. Although the nfluence of ths two parameter s very less when compared wth the effect of Ip on MRR. Fgure 1: Effect of Ip & Ton on MRR Fgure: 1 shows the estmated response surface for Materal removal rate n relaton to the process parameters of Ip and Ton whle Toff reman constant at ther mddle value. It can be seen from the fgure, the MRR tends to ncrease sgnfcantly wth the ncrease n Ip for any value of Ton. However, the MRR tends to ncrease wth ncrease n Ton, especally at hgher Ip. Hence, Maxmum MRR s obtaned at hgh peak current 1 amp and hgh pulse on tme 60 µs n ths nvestgaton. Ths s due to ther domnant control over the nput energy. Fgure 3: Effect of Ton & Toff on MRR Fgure 4: Effect of Ip & Ton on TWR Fgure : Effect of Ip & Toff on MRR Fgure shows the estmated response surface for Surface Materal removal rate n relaton to the process parameters of Ip and Toff whle Ton remans constant at ther mddle value. The MRR tends to decrease wth ncrease n Toff. Hence, From Fgure 4 the TWR s found to have an ncreasng trend wth the ncrease of current and pulse on tme. TWR s ncreasng nonlnearly wth the current. Ths s obvous, as the Ip ncreases, the pulse energy ncreases, and thus more heat s produced n the tool work pece nterface that leads to ncrease the meltng and evaporaton of the electrode. One can nterpret that Ip has a sgnfcant drect mpact on TWR. 130 P a g e

5 mm 3 /mn acheved at 1 amp Current and 60 µs Pulse on tme. Pulses off tme have very lttle effect on MRR and TWR. MRR s reduced sgnfcantly by ncreasng the Pulse off tme. TWR s found to have an ncreasng trend wth the ncrease of pulse on and pulse off tme. Ths establshes the fact that TWR s also proportonal to the total machnng tme wth rate of energy suppled. Fgure 5: Effect of Ip & Toff on TWR From Fgure 5 the TWR s found to have an ncreasng trend wth the ncrease of peak current and pulse off tme. It s observed that the TWR values are hgh when Ip s hgh wth hgher Toff or Toff s low wth hgher Ton. Fgure 6: Effect of Ton & Toff on TWR Fgure 6 shows that wth ncrease n Ton the TWR s reduced at any value of Toff. Wth ncrease n Toff the TWR ncreases at low value of Ton. Ths establshes that TWR s also proportonal to the total machnng tme wth rate of energy suppled. V. CONCLUSION Ths study hghlghts the development of a comprehensve mathematcal model for correlatng the nteractve and hgher order nfluences of varous electrcal dscharge machnng parameters through response surface methodology (RSM), utlzng relevant expermental data as obtaned through expermentaton. The research fndngs of the present study based on RSM models can be used effectvely n machnng of AISI 1045 steel n order to obtan best possble EDM effcency. MRR and TWR are found to have an ncreasng trend wth the ncrease of current and pulse on tme Maxmum MRR VI. REFERENCES [1]. Puertas. C.J. Lus. G. Vlla. Spacng roughness parameters study on the EDM of slcon carbde, Journal of Materals Processng Technology Vol : pp , 005. []. Garg R. K.. Sngh K. K.. Sachdeva Ansh. Sharma Vshal S.. Ojha Kuldeep. Sngh Sharanjt. Revew of research work n snkng EDM and WEDM on metal matrx composte materals, Int J Adv Manuf Technol Vol.50: pp , 010. [3]. Pradhan Mohan Kumar & Bswas Chandan umar. Neuro-fuzzy and neural network-based predcton of varous responses n electrcal dscharge machnng of AISI D steel, Int J Adv Manuf Technol Vol.50: pp , 010. [4]. Sameh S. Habb. Study of the parameters n electrcal dscharge machnng through response surface methodology approach Appled Mathematcal Modellng 33: , 009. [5]. C.H. Che Heron, B. Md. Deros, A. Gntng and M. Fauzah Investgaton on the nfluence of machnng parameters when machnng tool steel usng EDM Journal of Materals Processng Technology Vol 116: pp 84-87, 001 [6]. B B Patel, K B Rathod. Mult-Parameter Analyss and Modelng of Surface Roughness n Electro Dscharge Machnng of AISI D Steel Internatonal Journal of Scentfc & Engneerng Research, Volume 3, Issue 3, 01 [7]. B B Patel, K B Rathod. Mult-Parameter Analyss and Modelng of AISI D n Electro Dscharge Machnng Internatonal J. of Engg. Research & Indu. Appls. Vol.5, pp , P a g e