Optimization Of Machining Parameters In Wire Cut EDM Of Stainless Steel .

Advanced Materials Manufacturing & Characterization Vol. 8 Issue 1 (2018)Advanced Materials Manufacturing & Characterizationjournal home page: www.ijammc-griet.comOptimization of Machining Parameters in Wire Cut EDM of StainlessSteel 304 Using Taguchi TechniquesR.Venkatesha, V.R.Lenina, S.Vignesha, A.Krishnarajub, R.Ramkumarb, V.MoorthicaR.Venkatesh, V.R.Lenin, S.Vignesh (Assistant Professor, Mechanical Engineering, Mahendra Institute of Engineering and Technology,Namakkal, Tamilnadu, India)bA.Krishnaraju, R.Ramkumar (Assistant Professor, Mechanical Engineering, Mahendra Engineering College, Namakkal, Tamilnadu, India)cV.Moorthi (Assistant Professor, Mechanical Engineering, Annapoorana Engineering College, Salem, Tamilnadu, India)ABSTRACTThis paper presents the optimization of Wire ElectricalDischarge Machining (WEDM) process parameters suchas pulse on-time (Ton), pulse off-time (Toff) and wire feedrate (WF) to obtain the greatest material removal rate(MRR) and less surface roughness (Ra) of stainless steel304. The machining experiments were conductedaccording to the Taguchi parametric design (L9orthogonal array) using 0.25 mm diameter brass wire asa cutting tool. Signal to noise ratio (S/N) and Analysis ofvariance (ANOVA) were used to find the consequence ofeach parameter. Optimum cutting parameters havebeen verified through experiments. The results indicatethat pulse on-time is the most significant factorinfluencing the MRR and Ra followed by pulse off-timeand wire feed rate.Key Words: WEDM, Stainless steel 304, Taguchi, ANOVA1.0 IntroductionWEDM generate sparks, discharges between a smallwire electrode and a workpiece with de-ionized water asthe dielectric medium. Which erodes the workpiece andto produce complex shapes by numerically controlled(NC) path. The main goals of WEDM are to achieve abetter stability, higher productivity with a desiredaccuracy and surface finish in manufacturers and users.However, due to a large number of variables even ahighly skilled operator with a state of the art WEDM israrely able to achieve the optimal performance. Aneffective way to solve this problem is to determine therelationship between the performance measures of the[1]process and its controllable input parameters .Investigations into the influences of machining inputparameters on the performance of WEDM have beenreported widely. Several attempts have been made todevelop a mathematical model of the process. In thisstudy, the MRR and Ra of the machined workpiece weretaken into account as measures of the processperformance. Hence, investigations were carried out tostudy the effect of spark on-time, spark off-time andwire feed rate on the Ra characteristics and MRR. Insetting the machining parameters, the main goals of themaximization of MRR with minimization of Ra wereconsidered. A suitable selection of machiningparameters for the WEDM process is mostly relying onthe operator’s experience and manufacturer guidelines.Machining parameter table provided by themanufacturer is more generic in nature and does notaddress recent materials. Hence the need to optimizethe parameters for newer / advanced materials rises.Various research works have been carried out in WEDMof advanced materials. The following paragraphsummarizes the outcome of those researchers.[1]A study made by S.T.Newman, et al., reveals variousresearch areas like optimizing process variablesmonitoring and controlling the process wire EDMdevelopments. A theoretical and experimental study is[2]done by Jerzy, et al.,employed silver coatingtechnique to minimize the change in resistance affordedby workpiece material. The study on the machining[3]parameters optimization WEDM by Y.S.Liaorevealsthat Taguchi quality design method and analysis ofvariance can be used to find out significant parameterswhich affect machining performance of WEDM. Anexperimental study on effect cutting parameters on[4]surface roughness by Mustafa Than Goklerselectedsuitable cutting and offset parameter communication. Inorder to get good surface finish by creating table charts.An experiment carried out by S.S Mahapatra and[5]A.Panaikwas thought design genetic algorithm tomeasure various parameters and to optimize themachining process. A non-linear regression analyses andmathematic modeling are used for performancemeasurement. Corresponing author R.VenkateshE-mail address: Doi: http://dx.doi.org/10.11127/ijammc2018.03.04 Copyright@GRIET Publications. All rightsreserved.22

Table.1 Levels of factors used in the experimentThe present work describes the effect of cuttingparameters in WEDM of stainless steel 304. Hence thisresearch attempts to the study the optimum cuttingparameters for machining of stainless Steel 304 inWEDM by using Taguchi design methodologies.Sl.No.Symbol1A2B3C1.1 Taguchi MethodA statistical technique of Taguchi method used foranalyzing and optimizing the process parameters. TheTaguchi analysis uses orthogonal arrays from the designof experiments, theory to study the power of a largenumber of variables on responses with a small numberof experiments. In this method, the experimental resultsare changed into a signal-to-noise (S/N) ratio. It uses theS/N ratio as a measure of quality characteristics[6]deviating from or nearing the desired values . Taguchiclassified the quality characteristics into three categoriessuch as Lower the better, Higher the better and Normalthe better. These formulas used for calculating S/N ratiois as follows.The characteristics that lower value represents bettermachining performance, such as surface roughness iscalled “lower is better (LB)” and that higher valuesrepresent better machining performance, such as thematerial removal rate is called “higher is better (HB)” inquality engineering. The S/N ratio (signal to noise) couldbe an effective representation to find the significantparameter by evaluating the minimum variance. Theequations for calculating the S/N ratio are,22“Lower is better” (LB) S/N ratio - 10 log (1/r (y1 y2 22y3 . yn ))(1)2“Higher is Better” (HB) S/N ratio - 10 log (1/r (1/y1 2221/y2 1/y3 . 1/yn ))(2)Where,y1, y2, yn observed response values and n number ofreplications.CuttingParametersPulseon-time (Ton)Pulseoff-time (Toff)Wire feedrate (WF)Levels12 3Units15 8µ Sec105 2µSec12 3m/minTable.2 Standard L9 Orthogonal arrayExperimentNo.LevelsPulse offtime(Toff)123123123Pulse ontime(Ton)111222333123456789Wire FeedRate (WF)1232313121.3 Experimental Set upTable.3 Chemical composition of grade 304 .040.0320-100.1By applying the equation 1, the S/N values of theobtained Ra values are computed. By applying theequation 2, the S/N values of machining performance ofthe obtained MRR values are computed. In order toobtain the effects of machining parameters for eachlevel, the S/N values of each fixed parameter and levelin each machining performance were summed up.1.2 Process Parameters SelectionIn this analysis, WEDM parameters such as Ton, Toff andWF were considered. According to Taguchi’s design ofexperiments, for three parameters and three levels L9Taguchi orthogonal array [L9 OA] was selected. Thenumber of factors and their corresponding levels are3shown in the Table 1 and the basic Taguchi L9 (3 )orthogonal array used for this work is shown in Table 2.Fig.1 Experimental setup23

The experiments were conducted on a CNC WEDM. Thetrials conducted based on the settings shown in the L9orthogonal array. Stainless steel (grade 304) materialswere used as the workpiece. The surface roughnessesare measured on the machined surface using surf test211 machine. The surface roughness-measuring deviceis slid on the workpiece and readings are taken in themiddle of each test specimen. The material removal rateis calculated by loss of weight / time taken for each trial.The work material, electrode and other machiningsettings are as followsWork piece (anode)Electrode (cathode)Work piece thicknessVoltage: stain less steel. (Grade 304): 0.25 mm diameter brass wire.: 10 mm.: 80 V.1.4 Calculation of Material Removal Rate (MRR) andSurface RoughnessTo optimize the machining process parameters, themost important outcomes of WEDM such as MaterialRemoval Rate (MRR) and Surface Roughness (Ra) wereconsidered in this investigation. The Material Removalrate was calculated asMRR : (Length of cut x Spark gap x thickness)/Time takenThickness of job: 10 mmSpark gap: 0.4 mmAfter machining the workpiece, the surface roughnessvalues were measured using a Surf test 8513.012.238223.122.34Table.5 Response table for Means of .5432.0702.3030.7602WF2.1331.8531.9300.2803MRR was analyzed to determine the effects of WEDMprocess parameters. The experimental results werechanged into S/N ratio using MINITAB 18 and calculatedthe main effects at all levels of chosen parameters listedin table 5. The main effect for mean and S/N ratio isplotted in figure 2 and 3 respectively. In figure 2 and 3the MRR is greatest at the level 3 of Ton, at the level 3 ofToff and at the level 1 of WF. It is clear that the highestratio of S/N is the optimal level of each processparameter, therefore both the mean effect and S/Nratio values point out that the MRR is at the maximumwhen Ton at 8, Toff at 2 and WF at 1.1.5 Results and Discussion1.5.1 Process Parameters Influence on Metal RemovalRateStatistical analysis software MINITAB 18 was used foranalysis, the design of experiments to perform theanalysis of Taguchi and ANOVA to create regressionequations. In Taguchi method, the optimization ofprocess parameters provides the effect of individualindependent parameters on the identified qualitycharacteristics. The statistical analysis of variance wasconducted. The role of each parameter in influencingthe variation in quality characteristic was calculatedbased on the ANOVA. The ANOVA also suggest theprocess parameters which are statistically significant.The table 4 of results for MRR and Ra was revealed withthe input parameters.Fig.2 Main effects plots for mean of MRR.Table.4 Response 1.22Fig.3 Main effects plots for S/N ratio of MRR.24