Modeling of Wire Electrical Discharge Machining of AISI D3 Steel using Response Surface Methodology

Transcription

1 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 214) December 12 th 14 th, 214, IIT Guwahati, Assam, India Modeling of Wire Electrical Discharge Machining of AISI D3 Steel using Response Surface Methodology Brajesh Lodhi 1, Sanjay Agarwal 2* 1 Bundelkhand Institute of Engineering & Technology, Jhansi, U.P , INDIA 2* Bundelkhand Institute of Engineering & Technology, Jhansi, U.P , INDIA, sanjay72ag@rediffmail.com Abstract This paper present set of parameters that exhibit flexibility, frequent and miscellaneous collection based on experience and technology. It suggests an experimental analysis to establish the parameters location during the machining of AISI D3 steel. Response Surface Methodology (RSM), a powerful tool for experimental design, is used to optimize the CNC-wire cut EDM parameters. A central composite rotatable design (CCRD) has been used to develop the mathematical models involving input parameters and responses. The input parameters like pulse-on time, pulse-off time, peak current and wire feed have been taken for experimentation. The responses to be measured are MRR and surface roughness. Results indicates that pulse on time and pulse off time are directly proportional to the surface roughness where as peak current and wire feed is inversely proportional to the surface roughness. It is also observed that at higher value of wire feed and lower value of pulse on time, the MRR is maximum. At the same time, for the higher value of pulse on time and for the lower values the wire feed, MRR is lower. Further the values predicted by the developed mathematical models yielded results which agree reasonably well with the experimental results. 1. Introduction Wire electrical discharge machining (WEDM) has become a significant nontraditional machining process, extreme and extensive used in the aerospace, automotive and many industries. WEDM is a elementary operation in quite a lot of manufacturing processes in a magnitude of industries, which may possibly do with assortment precision and accuracy are of great vital. Spedding and Wang (1997) in organize to progress the presentation namely the surface roughness, cutting speed, dimensional accuracy, and material removal rate of the WEDM process quite a delivery of researchers have attempted in the earlier period. Conversely, the full prospective spending of this process is not exclusively solved since of its intricate and stochastic environment and the enlarged number of variables occupied in this operation. Chiang and Tsai (28) have been conducted experimental results indicate that the EDMed surface has the effect of silicon particles has the more ridge density, layer thickness and surface roughness on the surface be likely to increase with increasing and area fraction of the silicon particles. Hewidy et al. (25) has been conducted an experimental investigational has wear ratio increase with the increase of the peak current and surface roughness with the increase of duty factor and wire tension. The generation of arcing and after the certain limits, the increase of the peak current leads to the decrease of material remove rate. Cabanes et al. (28) has been prepared an investigation on-line supervision system that monitors and diagnoses in advance degraded cutting regimes in WEDM. The phenomena have been identified the process performance by readjusting the machining parameters depending on different levels of wire braking. Kanlayasiri et al. (27) have been revise the machining variables were Peak current, pulse-on time, pulse-off time and wire tension. The quantitative testing on residual analysis was used on the qualitative testing techniques. The pulse-on time and Peak current are significant a choice of two parameters increase to the surface roughness of wire EDM machined DC53die steel. Aminollah et al. (28) has been investigated the optimization of process parameters of power, time-off, voltage servo, wire tension, wire speed, and rotational speed on surface roughness and roundness in the turning wire electrical discharge machining. It concluded only power has a significant result on the surface roughness and none of the factors had a significant result on roundness. Study conducted by Kumar and Agarwal (212) have been conducted on MO optimization with conflicting objectives to yield a set of solutions known as trade-off, non-dominated, non-inferior or pareto-optimal solutions. Sankar et al. (26) has been investigated the feed forward back-propagation neural network models to passed out the optimization of wire electrical discharge machining parametric to maximized the 257

2 Modeling of Wire Electrical Discharge Machining of AISI D3 Steel using Response Surface Methodology cutting speed while keeping the surface roughness within the limits. Spedding and Wang (1997) have been conducted on modeling of response surface methodology and an artificial neural network has been specific drawbacks in applied commonly to model the process. The artificial neural network has a black box working characteristic has not been found the quantitative correlation between the input parameters and the output variables. Schoth et al. (25) the wire electrical discharge machining process for the hardness and strength of the difficult to machine the work material are no longer the depending factors that affect the tool wear for the machining process. Qu et al. (22a, 22b) investigate a mathematical for the material removal rate and the surface integrity and roundness of CWEDT process using the work material are brass and carbide. The design of experiments (Montgomery (2)) was conducted to perform more accurate, more efficient, and less cost. The literature review suggests that in relation to AISI D3 steel, the relationships between WEDM parameters and the machining characteristics such as surface roughness and MRR require further investigation. In the present investigation, experiments were conducted using the RSM experimental design method by considering pulse on time, pulse off time, peak current and wire feed as typical process parameters. Finally mathematical models was developed with pulse on time, pulse off time, peak current and wire feed to find out the surface roughness and MRR for the range and control of the process variables. The levels of significance on the surface roughness and MRR were statistically evaluated by using analysis of variance (ANOVA). 2. Experimentation The experimental studies were performed on a ELECTRONICA SUPER CUT 734 WEDM machine tool. The composition of AISI D3 steel work-piece material used for experimentation in this work is a given in Table 1. Zinc coated brass wire with.25 mm diameter (9 N/mm 2 tensile strength) was used in the experiments. The parameters, selected for different settings of pulse on time, pulse off time, peak current and wire feed were used in the experiments (Table 2). Table 1 Chemical composition of AISI D3 steel (wt %) Material C Cr Mn Ni Mo V Si AISI D Table Machining settings used in the experiments parameter Unit Level Pulse on time µs Pulse off time µs Peak current A Wire feed mm/min Table-3 Fixed parameters Wire Zinc coated brass wire of diameter.25 mm Shape and size of work-piece Rectangular piece of mm Dielectric fluid Deionized water Conductivity of 2 mho dielectric fluid (a (b) Fig. 1 (a) Experimental setup; (b) Machining zone. The photographic view of the machine and machining zone has been shown in Fig.1 (a) and (b) respectively. The other details of the experimentation have been shown in Table 3. The surface roughness was measured by with Mitutoyo Surftest SJO1P on the work-piece after machining. The average surface roughness height was used to estimate the surface roughness of the formed surface. The device was adjusted to the following values: measuring range, measuring speed and sampling length. In this study, for ease of organize and facility of being maintained at the preferred level, four separate variable parameters, pulse-on time, pulse-off time, peak current and wire feed have been considered. Trial runs were conducted as design of experiment. The upper and lower order was coded as +2 and correspondingly. The coded values can be calculated from the relationship X i = 2 [2X - (X max + X min )] / (X max - X min ) Where X i is the essential coded value of a variable X, when X is any value of the variable from X min to X max, X max to X min are the upper limit and lowest limit of the variables. 3. Developing the design matrix The design matrix was used in response surface methodology which is an experimental modeling of polynomials as limited approximations to achieve the exact input and output interaction. The several modules 257

3 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 214) December 12 th 14 th, 214, IIT Guwahati, Assam, India Table 4 Central composite design for experimentation Exp. R T No. on T off I p W a MRR f (µm) (mm 3 /min) of response surface methodology in the central composite rotatable design, which is capable of actually partition involved in two subsets of points: the first subset approximate linear and two factor interaction effects even as the second subset approximate curvature effects. Central composite rotatable designs were extremely efficient, given that much sequence on experiment variable effects and usually experimental error in a minimum number of required runs. The chosen Central composite rotatable design matrix as shown in Table 3 consists of 31 set of coded conditions composed of a full factorial 24 = 16 plus seven center points and eight star point (Table 4). All WEDM variables at the intermediate () level constitute the center point whereas the mixture of both variable at either its lowest order () or its higher order (+2) with the other three variables at the intermediate levels represent the star point. Accordingly, the 31 experimental runs allowed the evaluation of the linear quadratic and two way interactive effect of the WEDM variable on the responses. 5. Development of mathematical models During the make use of the design of experiments and applying regression analysis, the modeling of the preferred response to quite a small quantity of independent input variables can be gained. But the whole variables are unspecified to be considerable, the response surface be capable of exist expressed as follows. Y = f (X 1, X 2, X 3 ) (1) Y is the consequent response, X i (1, 2, 3, 4.) were the coded levels. The response function representing the WEDM performance can be expressed as follows: Y=b +b 1 X 1 +b 2 X 2 +b 3 X 3 +b 4 X 4 +b 11 X 2 1 +b 22 X 2 2 +b 33 X b 44 X 2 4 +b 12 X 1 X 2 +b 13 X 1 X 3 +b 14 X 1 X 4 +b 23 X 2 X 3 +b 24 X 2 X 4 +b 34 X 3 X 4 (2) Where b is the free term of the regression equation, the coefficient b 1,b 2,b 3,b 4 are linear terms, the coefficients b 11,b 22, b 33 and b 44 are the quadratic terms and the coefficients b 12,b 13, and b 24 are the interaction terms. Estimation of coefficient of the model the values of the coefficient of the above polynomial were calculated with the help of statistical software. 6. Checking the adequacy of the model developed The estimated coefficients obtained were used to create the model for the response parameter. The ability of the model developed was experienced by using the analysis of variance (ANOVA) technique as shown in Table 5. It was found that calculated values of F ratio were greater than the tabulated values at 95 % confidence level: hence the models are considered to be adequate. Another condition that is commonly used to show the adequacy of a fitted regression model is the coefficient of determination R 2 and adjusted R 2. For the models developed the designed R 2 and adjusted R 2 values are provided in Table 5 these values indicates that the regression models are quite adequate. 7. Testing the coefficients for significance From this ANOVA Table 5, surface roughness of the regression coefficient R for is.972. Calculate value is significant statically it can be proved the correlation coefficient value is significant at 95% confident level. From this table MRR of the regression coefficient R value is.962. Calculated value is significant statistically it can be proved the correlation coefficient value is significant at 95% confident level. The values of the regression coefficient give a suggestion since toward what extent the factors change the responses. Insignificant coefficients can be eliminated 257-3

4 Modeling of Wire Electrical Discharge Machining of AISI D3 Steel using Response Surface Methodology Table 5 Results of ANOVA for the models developed Response Sum of the squares Degree of freedom Mean square F-ratio P R 2 Adj Regression Residual Regression Residual Regression Residual R 2 R a MRR Table 6 Results of Confirmation test Measured Predicted Exp Parameters % Error values values No X 1 X 2 X 3 X 4 R a MRR R a MRR R a MRR without sacrificing much of the accuracy to avoid unwieldy mathematical effort. To attain this, t-test and F-tests are used. The test of significance was done by design the statistical software. For the period of backward steps, a variable is removed from the model and for the period of forward steps; a variable is added by design to the model. After determining the significant coefficient, the ultimate models were constructed by using merely these coefficients. 8. Developing the final model As determined by the procedures were the final mathematical models in terms of process parameter are given below: SR = T ON +.67 *T OFF -.31 *I P *W F + 36 *T ON (3) MRR= *T ON -.54*T OFF +.167*I P +.233*W f -.51*T ON *T OFF +.11*T ON *I P * T ON *W F -.227*T OFF *I p -.157*T OFF *W F *I P *W F +.31*T ON E-3*T 2 OFF +.534*I 2 2 p +.14*W F (4) 9. Confirmation experiments To verify the above developed regression equation 3 & 4, experiments were conducted. Three experimental runs were made using different process parameter values of pulse on time, pulse off time; peak current and wire feed in the design matrix and the surface roughness and MRR were measured using the same procedure as above. The obtained results were quite satisfactory and detail are presented in Table 6 1. Effect of working parameters on the surface roughness Figure 4 shows the variation surface roughness for the variables pulse on time, pulse off time, peak current and wire feed. From Figure 4 it is understood that, pulse on time (15-125µs), pulse off time (43-63µs) are directly proportional to the surface roughness where as peak current (15 3 A) and wire tension (42) is inversely proportional to the surface roughness. Figure 5 shows the pictorial representation of effect of peak current and pulse off time on the surface Surface roughness (Ra) Wire feed Figure 4 Main Effect plot for Surface roughness Ip Toff 1. Figure 5 Surface plot of Surface Roughness vs. peak current and pulse off-time roughness, when pulse on time and wire feed remains constant. From Figure 5 it is observed that at higher value of pulse off time and lower value of peak current, the surface roughness is maximized. At the same time, for the higher values of peak current and for the lower values the pulse off time, surface roughness is minimized

5 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 214) December 12 th 14 th, 214, IIT Guwahati, Assam, India MRR Wire feed Figure 6 Main effects plot for MRR The goodness of fit for the above equation has proved significantly using statistical significance test. For example by doing the experiment if pulse on time is 19, pulse off time is 51, peak current value 15 A, and wire feed is 3, by substituting this value in the above said equation we get the surface roughness value as 1.742µm which is reflected in the Figure 5, hence the obtained equation has good significant. Figure 6 shows the variation of MRR with respect to pulse on time, pulse off time, peak current and wire MRR Wf.. - Ton 1. Figure 7 Surface plot of MRR vs. peak current and wire feed feed. MRR is inversely and linearly proportional to pulse on time, directly and linearly proportional to wire feed. MRR is non-linearly varied with the pulse off time. MRR remains constant for the peak current range of 15A to 18A. The wire tension is linear and directly proportional to the MRR. Similarly Figure 7 shows the pictorial representation of effect of peak pulse on time and wire feed on the MRR. For high value of pulse on time the MRR is only 5 % of the MRR with low pulse on time for high wire feed. The goodness of fit for the above equation has proved significantly using statistically significance test. For example, the experimental value for pulse on time 19, pulse off time 51, peak current 15 A and wire feed 3, the MRR is.17. The negative indicates that MRR is inversely proportional which is reflected in the Figure 7. Hence the obtained equation represents good fit. 11. Conclusions In this research paper, wire electrical discharge machining process parameters are optimized using the response surface methodology. The surface roughness is investigated experimentally for the variation of pulse on time, pulse off time, peak current and wire feed. It has been observed from the above discussion that the variation of surface roughness with the variation of peak current is not appreciable. Hence the peak current variation is not having much effect on surface roughness. It is also been inferred that at higher value of pulse off time and lower value of peak current, the surface roughness is having its maximum value i.e. 2.4µm. At the same time, for the higher values of peak current and for the lower values the pulse off time, surface roughness is having lower value i.e. 1.3µm. It is also observed from the above results that pulse on time and pulse off time are directly proportional to the surface roughness whereas peak current and wire feed is inversely proportional to the surface roughness. Further the MRR is also investigated experimentally for the variation of pulse on time, pulse off time, peak current and wire feed. It is inferred that at higher value of wire feed and lower value of pulse on time, the MRR is maximum. At the same time, for the higher value of pulse on time and for the lower values the wire feed, MRR is lower. The MRR is directly proportional to pulse on time and inversely proportional to wire feed. MRR decreases for the increasing of pulse off time. Pulse off time is optimized. Figure 6 shows that at the pulse off time of 58µs the MRR is minimum REFERENCES Aminollah M., Alireza Fadaei T., Ehsan E., and Davoud K. (28) A new approach to surface roughness and roundness improvement in wire electrical discharge turning based on statistical analyses, International Journal of Advanced Manufacturing Technology,Vol. 39, pp Cabanes I, Portillo E, Marcos M. and Sanchez JA. (28) On-line prevention of wire breaking in wire electro-discharge machining. Robot CIM-Integrated Manufacturing, Vol. 24, pp Hewidy M.S., El-Taweel T.A. and El-Safty M.F. (25) Modelling the machining parameters of wire electrical discharge machining of Inconel 61 using RSM, 257-5

6 Modeling of Wire Electrical Discharge Machining of AISI D3 Steel using Response Surface Methodology Journal of Material Processing Technology, Vol. 169, pp Kanlayasiri K. and Boonmung S. (27) Effects of wire-edm machining variables on surface roughness of newly developed DC 53 die steel: Design of experiments and regression model, Journal of Material Processing Technology, Vol , pp Ko-Ta Chiang and De-Chang Tsai. (28) Effect of silicon particles on the rapidly resolidified layer of Al- Si alloys in the electro discharge machining process, International Journal of Advanced Manufacturing Technology, Vol. 36, pp Kumar K. and Agarwal S. (212) Multi-objective parametric optimization on machining with wire electric discharge machining, International Journal of Advanced Manufacturing Technology, Vol. 62, pp Montgomery, DC. (2) Design and Analysis of Experiments. John Willy & Sons Inc. Qu J., shih A.J. and Scattergood, R.O. (22a) Development of the cylindrical wire electrical discharge machining process, part 1: Concept, design and material removal rate, Journal of Manufacturing Science and Engineering Trans. of ASME, Vol. 124(3), pp Qu J.., shih A.J. and Scattergood R.O. (22b) Development of the cylindrical wire electrical discharge machining process. Part 2: Surface integrity and roundness, Journal of Manufacturing Science and Engineering Trans. of ASME, Vol. 124 (3), pp Sankar S., Mitra S. and Bhattacharyya B. (26) Parametric optimization of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model, International Journal of Advanced Manufacturing Technology, Vol. 27(5-6), pp Schoth A., Forster R. and Menz W. (25) Micro-wire EDM high aspect ratio 3D micro-structuring of ceramics and metals. Microsystem Technology, Vol. 11, pp Spedding T.A. and Wang Z.G. (1997) Parametric optimization and surface characterization of wire electrical discharge machining process. Precision Engineering, Vol. 2(1), pp. 55. Spedding T.A. and Wang Z.Q. (1997) Study on modeling of wire EDM process, Journal of Material Processing Technology, Vol. 69, pp