A Study on Effect of EDM Process Parameters on AISI 304L Stainless Steel

Transcription

1 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India A Study on Effect of EDM Process Parameters on AISI 304L Stainless Steel D. K. Ojha 1*, S. Panda 2, D.Mishra 3 1* Department of Production Engineering, Veer Surendra Sai University of Technology, Burla , idkojha@gmail.com 2 Departmentof Production Engineering, Veer Surendra Sai University of Technology, Burla , sumanta.panda@gmail.com 3 Departmentof Production Engineering, Veer Surendra Sai University of Technology, Burla , dmvssut@gmail.com Abstract Electro Discharge Machining (EDM) has become an important and cost-effective method of machining extremely tough and brittle electrically conductive materials. It is widely used in the process of making moulds, dies, sections of complex geometry and intricate shapes. The work piece material selected for this study is AISI 304L Stainless steel. In the present work the effect and percentage contribution of various machining parameters on output parameters is studied using Taguchi s method and ANOVA analysis. A hybrid Taguchi based grey relational analysis is proposed for multi objective optimization of performance variables i.e. high material removal rate, low tool wear rate, better surface finish with lower dimensional tolerance. Further a regression analysis can be used for finding the model equations for various performance parameters. The input parameters considered in this study are dielectric flow rate, discharge current, Pulse on time (T on ) and Pulse off time (T Off ). The tool material used is copper. Analysis of variance is used to study the significance of process variables on Material Removal Rate (MRR), Tool Wear Rate (TWR), Surface Roughness (R a ), Dimensional Tolerance (DT). The analysis using Taguchi method reveals that discharge current significantly affects MRR, Dimensional Tolerance and R a whereas TWR is mostly affected by flow rate of the dielectric used. A comparison of the hybrid approach and Taguchi analysis is presented in this study. The confirmation test supports the result of the proposed hybrid Grey-Taguchi analysis. Keywords EDM, ANOVA, AISI 304L Stainless Steel, Grey Relational Analysis, Taguchi method 1 Introduction Electrical discharge machining (EDM) is one of the most extensively used non-conventional material removal processes. Its unique feature of using thermal energy to machine electrically conductive parts regardless of hardness has been its distinctive advantage in the manufacture of mould, die, automotive, aerospace and surgical components. In 2 Literature Review The origin of electrical discharge machining goes back to 1770, when English scientist Joseph Priestly discovered the erosive effect of electrical discharges. EDM is a non-traditional concept of machining which has been widely used to produce dies and moulds and also finishing parts for aerospace and automotive industry and surgical components. addition, EDM does not make direct contact between the electrode and the work-piece eliminating mechanical stresses, chatter and vibration problems during machining. Today, an electrode as small as 0.1 mm can be used to drill holes into curved surfaces at steep angles without drill wander. Haron et al. (2001) performed a series of investigations in EDM process using AISI 1045 tool steel and copper electrodes of various diameters to establish the correlation between the pulse current and the corresponding material removal rate and electrode wear rate, respectively

2 A Study on Effect of EDM Process Parameters on AISI 304L Stainless Steel Tomadi et al. (2009) invested the machining of tungsten carbide with copper tungsten as electrode. The full factorial design of experiments was used for analysing the parameters. The primary influencing parameters for Surface Roughness (R a ) were voltage and pulse off time while current and pulse on time were the secondary parameters. For Material Removal Rate (MRR) the most influential was pulse on time followed by voltage, current and pulse off time. Finally in case of Tool Wear Rate (TWR) the important factor was pulse off time followed by peak current. Rahmanet al. (2011) investigated the effect off the peak current and pulse duration on the performance characteristics of the EDM of Stainless Steel 304. It was reported that the current and pulse on time greatly affected the MRR, TWR and R a. The MRR increases almost linearly with the increasing current, the R a increases linearly with current for different pulse on time, It was further observed that TWR increased with the increase in peak current and decreases when the pulse on time was increased. Patel et al. (2011) studied the influence of machining parameters on MRR, TWR, and R a during EDM of Al 2 O 3 SiCw TiC ceramic composite using copper electrode. Mathematical models were developed using Response Surface Methodology (CCRD) to predict MRR, EWR, and R a by correlating the input parameters, namely, discharge current, pulse-on time, duty cycle, and gap voltage. Significant parameters were identified for each response. ANOVA was used to establish adequacy of the developed models. The developed models have also been validated using chi square test. The results show that second-order models developed for MRR and EWR are statistically significant. There are limited nos. of research articles taking only the pressure of the dielectric used as an input parameter rather than the flow rate of the dielectric. Here the flow rate of dielectric is taken as an input parameter and significant effects are seen. 3 Methodology 3.1 Taguchi Method Taguchi method is systematic and efficiency approach to find the optimal combination of input parameters. This method utilizes the orthogonal array of experiments to reduce the number of experiments in any machining process. Since four input parameters have been selected, L 9 orthogonal array has been selected for this study. Using the orthogonal array 9 experiments have been conducted with each of 2 replicates. It is also used to study the effects of input parameters on respones. This quality analysis tool analyzes the obtained results by using signal-to-noise ( ). This ratio is determined by characteristics of the machining process. The catagories of this ratio are larger the better, smaller the better and nominal the best. In EDM process, the main aim is to increase MRR, reduce TWR, R a, DT. Hence, Larger the better has been applied for MRR whereas Smaller the better has been applied for TWR, R a, DT. For Larger the better, (1) For Smaller the better, (2) 3.2 Grey Relational Approach In the present study, the multiple performance characteristics have been investigated with grey relational approach. In this method, the multiple Step 1 For larger the better, For smaller the better, The S/N ratios obtained from the Taguchi analysis have to be normalised in the range of 0 to 1 using the above two equations. Where is the normalized, is the obtained from the Step 2 performance characteristics can be converted into single grey relational grade. The following steps are done for this approach. (3) (4) Taguchi analysis, the and are maximum and minimum values of respectively. Grey relational coefficient (5) 360 2

3 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India The grey relational co-efficient indicates the relational degree between every sequence of obtained values. Since multi-response characteristics consist of both larger the better and smaller the better, is assumed to 0.5 in this case. and are the minimum and maximum absolute difference which is a deviation Step 3 from target value and can be treated as qualityloss. Where. denotes the sequence and denotes the comparability sequence. is distinguishing or identified coefficient. Grey relational grade (6) Where m is the number of responses. The high value of grey relational grade indicates the stronger relational degree between ideal sequence and present 4 Experimental Details AISI 304L stainless steelis chosen as the workpiece material and copper material is used as the tool electrode material with commercial kerosene as Table 1Properties of Workpiece and Tool Material sequence. Ideal sequence is the best sequence in the machining process. Higher grey grade indicates closer to the optimal response in the process. dielectric. Properties of workpiece and tool electrode materials are given in Table 1. Properties Workpiece Tool Melting Point Temp C C Elastic modulus(e) 193 GPa 123 GPa Poissons ratio Density 8.03 g/cm g/cm 3 Table 2 Composition of AISI Stainless Steel 304L Chemicals C Mn Cr Ni Si P S Composition wt% Table 3 Process Parameters Input Factors Responses, Measured units Flow Rate Material Removal Rate (MRR), g/sec Peak Current (I p ) Tool Wear Rate (TWR), g/sec Pulse on time (T on ) Surface Roughness (R a ), µm Pulse off time (T Off ) Dimensional Tolerances (DT), mm Design of Experiment Table4Experimental factor and its Levels Symbol Factors Unit Levels A Flow Rate Ltr/hr B Peak Current Amp C T on µsec D T Off µsec Table 5 L 9 Orthogonal Array Design Matrix of the Experimental Runs and Results Expt. No. Input Factors (Coded Values) Input Factors (Actual Values) Responses (Average of Two Replicates for Each Experimental Run) A B C D A B C D MRR (g/sec) TWR (g/sec) R a (µm) DT (mm 2 )

4 A Study on Effect of EDM Process Parameters on AISI 304L Stainless Steel 4.2 Analysis of Experiment Figure 1 Main effect plots for MRR Figure 2 Main effect plots for TWR Figure 3 Main effect plots for R a Figure 4 Main effect plots for DT Table 6 Anova Table for MRR Model 1.906* * < A-A 2.632* * < B-B 1.086* * < C-C 4.201* * < D-D 1.370* * < Pure Error 3.150* * Total 1.909* R 2 = , Adj. R 2 =0.9969, Pred. R 2 = , Adeq. Precision = Table 7 Anova Table for TWR Model 7.319* * A-A 4.090* * < B-B 1.631* * ** 0.21 C-C 2.290* * D-D 9.227* * Pure Error 4.590* * Total 7.778* R 2 = , Adj. R 2 =0.8885, Pred. R 2 = , Adeq. Precision = Table 8 Anova Table for R a Model < A-A < B-B < C-C < D-D < Pure Error * Total R 2 = , Adj. R 2 =0.9956, Pred. R 2 = , Adeq. Precision =

5 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Table 9 Anova Table for DT Model < A-A < B-B < C-C < D-D < Pure Error 8.650* * Total R 2 = , Adj. R 2 =0.9999, Pred. R 2 = , Adeq. Precision = The above ANOVA tables are drawn by using Design Expert software (Stat-Ease, Minneapolis, USA). The Models F-value in the four casesare , 17.94, , respectively in the Tables 6, 7, 8, 9 implies the models are significant. There is only 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than indicate model terms are significant. In this case A, B, C, D are significant model terms for all the 3 cases except for TWR where factor B i.e. peak current is insignificant. As values greater than indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve our model. The "Pred R-Squared" is in reasonable agreement with the "Adj R-Squared". "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Here the ratio indicates an adequate signal. This model can be used to navigate the design space. 5 Results & Discussions Table 10 shows the with its corresponding means of grey relational grade to find out optimal Normalized, Grey Relational Coefficients, combination. It shows current has maximum effect & Grey Relational Grade ( ) & Rank for responses.the pulse on time has minimum effect on the multi higher value will have better multi response response charcteristics. characteristics. Table 11 shows response table for Table 10 Grey Relational Coefficient and Grades with their Ranks Ex No Normalized Grey Relational Coefficients Rank MRR TWR R a DT MRR TWR R a DT MRR TWR R a DT * * Here Experimental run no-9 indicates higher grey relational grade i.e. better multi response characteristics. Grey Relational Grade Experimental Runs Figure 6 Grey Relational Grade for Multi Response 360 5

6 A Study on Effect of EDM Process Parameters on AISI 304L Stainless Steel Table 11 Response Table for Average Grey Relational Grade Input Factors Symbols Average Grey Relational Grade by Factor Level Delta Rank Level 1 Level 2 Level 3 (Max-Min) Dielectric Flow Rate A * Current B * Pulse on time C * Pulse off time D * * Indicates optimal level for respective parameters. So optimal combination will be A 3 B 3 C 2 D 3 6 Confirmation Test After identifying the optimal process parameters, the confirmation test is to be conducted to validate the analysis. In the confirmation test, an experiment has been conducted with optimal process parameters Table 6 Confirmation Results settings. The predicted grey relational analysis & predicted responseswith optimal process parameters by using Minitab 16 software shown in Table 6. There is an improvement in Grey relational grade by 1.12%. Responses Optimal Process Parameters Level Predicted (A 3 B 3 C 2 D 3 ) Experimental (A 3 B 3 C 2 D 3 ) MRR(gm/sec) TWR(gm/sec) R a (µm) DT(mm 2 ) Grey Relational Grade improvement in the Grey relational grade 1.12 % 7 Conclusions In this study, Taguchi L 9 array with grey relational analysis has been used to optimize the multiple performance characteristics such as material removal rate, tool wear rate, surface roughness and dimensional tolerance. The rank has been found from the response table showing that peak current is most significant factor among process parameters involved in EDM process.the conclusions of this work are summarized as follows:the optimal parameters combination was determined as A 3 B 3 C 2 D 3 i.e. flow References C.H. Che Haron, B.Md. Deros, A. Ginting, M. Fauziah(2001), Investigation on the influence of machining parameters when machining tool steel using EDM, Journal of Material Processing Technology,Vol.116,pp Deng, J. L(1987), Basic methods of Grey system, Journal of Grey System, Vol.1, pp Iqbal AKM A., Khan A.A.(2011), Optimization of process parameters on EDM milling of stainless steel AISI 304, Advance Materials Research, Vol , pp K.M. Patel, Pulak M. Pandey, P. Venkateswara Rao(2011), Study on Machinabilty of Al 2 O 3 Ceramic Composite in EDM Using Respons Surface Methodology, Journal of EngineeringMaterials and Technology, APRIL-2011,Vol. 133/ P.J. Ross(1988), Taguchi Techniques for Quality Engineering, McGraw-Hill, NewYork. Rahman M.M., Khan M.A.R., Kadirgama K., Noor M.M. and Bakar R.A.(2011), Experimental rate at Ltr/hr,current at 7.5A, pulse ON time at 100µs and pulse OFF time at 500µs. Confirmation test proved that the determined optimum combination has satisfied the real requirement of input process parameters in the thermal erosion process. The predicted results were checked with experimental results and a good agreement was found.this work demonstrates the method of using Taguchi methods for optimizing the EDM parameters for multiple response characteristics. Investigation into EDM of Stainless Steel 304, Journal of Applied Sciences, pp S. Kalpajian, S.R. Schmid(2003), Material removal processes: abrasive,chemical,electrical and highenergy beam in ManufacturingProcesses for Engineering Materials, Prentice Hall, New Jersey, p S.R.Nipanikar(2012), Parameter Optimization Of Electro Discharge Machining Of Aisi D3 Steel Material By Using Taguchi Method, Journal of Engg. Research and Studies JERS/Vol. III/ Issue III/July-Sept./ Tomadi S.H., Hassan M.A., Hamedon Z.(2009), Analysis of the influence of EDM parameters on surface quality, material removal rate and electrode wear of tungsten carbide, Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol II