MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM

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1 MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM Swapan Barman 1*, Kousv Mondol 2, Nagahanumaiah 3, Asit Baran Puri 4 1* CSIR-Central Mechanical Engineering Research Institute, Durgapur sbarman@cmeri.res.in 2 Kanksa Academy of Technology and Management, Panagarh kousvmondol@gmail.com 3 CSIR-Central Mechanical Engineering Research Institute, Durgapur , naga@cmeri.res.in 4 National Institute of Technology (NIT), Durgapur , India abpuri2000@yahoo.co.in Abstract In this communication, an attempt was taken study the influence of machining parameters like gap voltage, capacitance and depth of hole on machining performances for 2D and 3D surface finish of blind micro holes and electrode wear length in micro electrical discharge drilling (MEDD) process. The experimentation was carried out employing full facrial design (3 3 ) with three levels of each machining parameter study simultaneous effect of facrs on machining performance. Mathematical models have been developed for the mentioned machining outputs by multiple linear regression analysis. Response surface methodology was utilized carry out multiple response optimization and find the optimum process parameter settings for a desired yield by conur overlapping method using statistical software MINITAB 16 with 95% confidence level and with levels of facrs in coded units. The models so developed are excellent for acceptance as the coefficients of multiple determinations (R 2 ) for the above responses were computed as 97.18%, 97.79% and98.90% respectively. From the models, it was revealed that capacitance, gap voltage and depth of hole influence R a, S a and electrode wear length very strongly. Surface plots and conur plots were generated find out the relationship between the machining parameters and the output. The optimum parametric settings for the machining parameters were computed from overlaid conur plots. The proposed models could be considered as valuable ols for the process planning of MEDD leading cost effective machining of blind micro holes in industry. Keywords : MEDD, surface texture R a and S a, full facrial design, optimization. 1 Introduction In micro EDM, material removal takes place through melting and evaporation by discharge pulses when the thermoelectric energy is created between a ol electrode and a work piece usually submerged in a dielectric fluid and a spark gap is maintained between them. Advancement in development of CNC systems and spark generars, helps micro EDM process be focused on production of complicated and high aspect ratio micro features with improved machined surface quality with very small forces and good repeatability of the process reported by D.T. Pham et al. (2004). I. Puertas et al. (2004) discussed in their paper that EDM is a wide spread technique used in industry for high precision machining of all type of conductive materials such as metals, metallic alloys, graphite and some ceramic materials regardless of their hardness. Due this advantage, micro EDM has become one of the most reliable methods used extensively in micro mold making, production of dies, cavities and many complex 3D structures communicated by I. Alting et al. (2003). In MEDD,the ol (electrode) rotates provide better uniformity in the generated hole profile. Micro holes are the most basic machined features of micro manufacturing secrs used for various applications like circuit board of integrated circuit packages, fuel injection nozzles, spinneret holes, bio-medical filters and others found in article of T. Masuzawa (2000). Manufacturing of micro holes depends on several process variables. Many research works were conducted analyze the process variables find out optimal conditions for micro features manufactured. To study the effect for process parameters on surface finish of 809-1

MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM blind micro holes is very difficult in non-destructive method due miniature size of the holes irrespective

2 MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM blind micro holes is very difficult in non-destructive method due miniature size of the holes irrespective of using high precision equipment and techniques. In this paper, an attempt was taken study the influence of machining parameters like gap voltage, capacitance and depth of hole on machining performances for 2D and 3D surface finish and electrode wear length in MEDD process on a Monel K-500 alloy plate using pure tungsten electrode. The experimentation was carried out employing full facrial design (3 3 ) with three levels of each machining parameters. A full facrial design allows study of simultaneous effects of several facrs on a process explained by D.C. Montgomery (2000) in his book. Mathematical models have been developed for the machining outputs by multiple linear regression analysis. Response surface methodology was utilized carry out multiple response optimization and find the optimum process parameter settings for a desired yield by conur overlapping method. Table 1 Machining parameters with their levels and coding identification Machining parameters Unit Varia Depth of hole, X1 mm ble Capacitance, X2 nf (f) Gap voltage, X3 V Fixed Spindle speed rpm Spark feed rate µm/s Threshold % Retrieval feed mm/ min Polarity Dir Coolant flow rate Levels m m 3000 s 4 60% 10 rect (workpiece +) constant 2 Experimentationand observation The objective of this study was investigate the influence of machining parameters like depth of hole (X1), Capacitance (X2) and gap voltage (X3) on 2D and 3D surface texture (R a and S a ) of blind holes and ol wear length (L). For this study, 27 micro holes were drilled on 6 mm thick Monel K-500 alloy plate with tungsten electrode of diameter 300 µm by a high precision, integrated multi-process machine ol for micro machining (model DT-110 of M/s. MikroolsPte Ltd, Singapore) with combinationn of machining parameters shown in Table 1.Properties of workpiece, electrode and dielectric fluid were presented in Table 2. Blind micro holes were opened axially by wire EDM (WEDM) (model :Elektra Supercut 634, make : Electronica Machine Tools, Pune). Taylor-Hobson, UK make high accuracy 3D optical surface profiler (model : CCI Lite, resolution : 0.1Å) was used scan the opened holes and measure R a and S a surface texture characteristics of opened holes. Marcel-Aubert, SA make optical microscope (model : MA-705-Z1) and Olympus, Japan make image analyzer (model : BX51M, camera : Olympus-E330) were used take theimages of the holes. The phograph of MEDD, images of p of the micro hole, opened micro holes and ol electrode were shown in Figure 1. Statistical software MINITAB 16 (trial version) was used for regression analysis and analysis of variance (ANOVA) assuming that a second order polynomial model could sufficiently represent the above input output relationship. The mathematical models have been developed for R a, S a and electrode wear length by multiple linear regression analysis with 95% confidence level with facr levels in coded units. (a) (b) (c) (d) Figure 1 Images of (a) MEDD, (b) p of blind hole, (c) ol electrode and (d) opened micro holes. Table 2 Properties of workpiece, ol electrode material and dielectric fluid Workpiece (MONEL K-500 alloy) Density Hardness Melting point Thermal conductivity Thermal expansion coefficient Tool electrode (Tungsten) Density Hardness Melting point Relative conductivity ( silver) Thermal expansion coefficient Dielectric fluid (EDM oil 3) Volumetric mass at 15ºC Viscosity at 20 ºC Flash point (Pensky-Martens) Au ignition temperature 8.44 g/cc HRC35 (max.) ºC 17.5 W/m K 13.7x10-6 K g/cc HRA ºC x10-6 K Kg/m mm 2 /s 134 ºC 470 ºF 809-2

Values of gap voltage and depth of hole shown in Table 1 were coded as follows: Coded value, = ( ) ( ) (1) Log scale was used code the values of capacitance as follows : Coded value, = ( ) (2) ( )

3 Values of gap voltage and depth of hole shown in Table 1 were coded as follows: Coded value, = ( ) ( ) (1) Log scale was used code the values of capacitance as follows : Coded value, = ( ) (2) ( ) The extracted profile of scanned surface of the opened micro hole for R a measurement, scanned surface and 3D surface of the opened holes number 7 and 8 were presented in Figure 2(a), 2(b) and 2(c) respectively. The observed values of electrode wear length calculated by taking length of electrode before and after machining and R a and S a values measured by 3D optical surface profilerwere given in Table 3. (b) (a) Figure 2 Imagesfrom 3D profiler : (a) Profile for R a measurement, (b) scanned surface data of opened hole 7 & hole 8 and (c) 3D images of hole7 & hole 8. 3 Results and Discussion As assumed that a second order polynomial model could sufficiently represent the selected input output relationship for the present study, so the regression analysis and ANOVA were performed considering following second order model of generalized form : (c) = β +β +β +β, (3) Table 3 Measured values of R a, S a and L Exp. No. R a (µm) S a (µm) L(mm) where, Y is the response (predicted), β 0 is the intercept, β i is the coefficient corresponding the facr X i, β ii is the coefficient of X i 2 and β ij is the coefficient of X i X j, for i, j = 1, 2,.., n, and i < j. For 3 responses X 1, X 2 and X 3, the above equation can be simplified as =β +β +β +β +β +β + β +β +β +β (4) Table 4 Coefficients of Regression Models Terms Coeff. of R a Coeff. of S a Coeff. of L Const X X X

MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM X1*X1 0.00522 0.00732 0.003078 X2*X2-0.89926-0.95879 0.000561 X3*X3-0.77854 0.26459-0.01451 X1*X2-0.08823 0.

The coefficients and summary of ANOVA were represented concisely in Table 4 and Table 5 respectively. Table 5 Summary of ANOVA for responses Source P values For R a For S a For L Regression 0.000 0.

002 Interaction 0.033 0.431 0.000 X1*X2 0.310 0.114 0.001 X1*X3 0.900 0.987 0.021 X2*X3 0.006 0.725 0.000 L 98.90% 96.67% 98.

07084*X2 +1.03641*X3+0.00522*X1 2-0.89926*X2 2-0.77854*X3 2-0.08823*X1*X2 +0.01074*X1*X3+0.26504*X2*X3 (5) Y2= 17.5534+0.44904*X1+1.66396*X2 +1.00494*X3+0.00732*X1 2-0.95879*X2 2 +0.26459*X3 2 +0.

014350*X2*X3 (7) The response surface plots for all responses were plotted against selected variable machining parameters and presented in Figure 3. Term with P-value for regression less than 0.

the models so developed are excellent for acceptance as the coefficients of multiple determinations, R 2 for the above responses were computed as 97.18%, 97.79% and98.90% respectively.

4 MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM X1*X X2*X X3*X X1*X X1*X X2*X So, by multiple linear regression analysis, mathematical models were developed for R a, S a and electrode wear length (L). The coefficients and summary of ANOVA were represented concisely in Table 4 and Table 5 respectively. Table 5 Summary of ANOVA for responses Source P values For R a For S a For L Regression Linear X X X Square X1*X X2*X X3*X Interaction X1*X X1*X X2*X L 98.90% 96.67% 98.32% From the above results, the mathematical models for 2D surface finish R a (Y1), 3D surface finish S a (Y2) and electrode wear length L (Y3) were obtained as follows : Y1 = *X *X *X *X *X *X *X1*X *X1*X *X2*X3 (5) Y2= *X *X *X *X *X *X *X1*X *X1*X *X2*X3 (6) Y3= *X *X *X *X *X *X *X1*X *X1*X *X2*X3 (7) The response surface plots for all responses were plotted against selected variable machining parameters and presented in Figure 3. Term with P-value for regression less than 0.05 is considered as significant in the fitted models. From the models, it was revealed that capacitance, gap voltage and depth of hole influence R a, S a and L very strongly.the models so developed are excellent for acceptance as the coefficients of multiple determinations, R 2 for the above responses were computed as 97.18%, 97.79% and98.90% respectively. R 2 is a measure of the amount of reduction in the variability of Y obtained by using the model. Focus on the model is maximize the adjusted R 2 and the predicted R 2 for acceptance as discussed by R. H. Myers et al. in their book (2009). From model summary statistics shown in Table6, it could be revealed that the models developed are excellent for acceptance as R 2, adjusted R 2 and predicted R 2 for the above responses were computed are very closer unity,as desired for a model, giving better its predictions for responses. Table 6 R-squared values for responses R-squared Values R 2 Predicted R 2 Adjusted R 2 R a 97.18% 93.14% 95.69% S a 97.79% 94.28% 96.62% 809-4

Variables X1 : Depth of micro hole X2 : Capacitance X3 :

finish, S a Y3 : Electrode wear length Figure 3Response

a and L were computed for gap voltage of 90V, capacitance

all the responses keep increasing with increase in gap

Gap voltage R a : 2D surface finish, S a : 3D surface

responses against machining parameters Conur plots shown

between the machining parameters and the responses.

parameters were computed from overlaid conur plots.

for selected range of responses were plotted.

selected values chosen for R a, S a and L were from 6.

considered as both are represent surface texture of the

5 Variables X1 : Depth of micro hole X2 : Capacitance X3 : Gap voltage Y1 : 2D surface finish, R a Y2 : 3D surface finish, S a Y3 : Electrode wear length Figure 3Response surface plots of R a, S a and L against machining parameters. From surface plots the minimum values of responses R a, S a and L were computed for gap voltage of 90V, capacitance of 1nF and depth of hole for 2 mm.all the responses keep increasing with increase in gap voltage, capacitance and depth of hole. Variables X1 : Depth of micro hole X2 : Capacitance X3 : Gap voltage R a : 2D surface finish, S a : 3D surface finish, L : Electrode wear length Figure 4 Conur plots of responses against machining parameters Conur plots shown in Figure 4 were generated find out the relationship between the machining parameters and the responses. The optimum parametric settings for the machining parameters were computed from overlaid conur plots. In Figure 5(a) 5(f) overlaid zones of machining parameters for selected range of responses were plotted. For finding out the overlaid zone of machining parameters, selected values chosen for R a, S a and L were from 6.2 µm 6.6 µm, from 17 µm 17.5 µm and from 0.15 mm 0.18 mm respectively. Optimum parametric settings between R a and S a were not considered as both are represent surface texture of the machined surfaces in 2D and 3D form respectively. Optimized zoneof machining parameters gap voltage, capacitance and depth of hole for selected range of R a, S a and L were tabulated in Table

MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM parameters on R a, S a and electrode wear length L by full facrial design.

The optimum process parameter settings for a desired yield were computed by conur overlapping methodof response surface methodology.

ii) The mathematical models developed were excellent for acceptance as coefficients of multiple determinations, R 2, adjusted R 2 and predicted R 2 for the selected responses computed were very

Variables X1:Depth of hole X2:Capacitance X3:Gap voltage R a : 2D surface finish, S a : 3D surface finish, L : Electrode wear length Figure 5Overlaid zone of machining parameters for selected range

6 MODELING AND OPTIMIZATION FOR DRILLING OF HIGH ASPECT RATIO BLIND MICRO HOLES IN MICRO EDM parameters on R a, S a and electrode wear length L by full facrial design. Mathematical models were developed for outputs by multiple linear regression analysis. The optimum process parameter settings for a desired yield were computed by conur overlapping methodof response surface methodology. It was observed that i)all considered machining parameters gap voltage, capacitance and depth of micro hole were very strongly significant for selected responses S a, R s and L. ii) The mathematical models developed were excellent for acceptance as coefficients of multiple determinations, R 2, adjusted R 2 and predicted R 2 for the selected responses computed were very closer unity. The proposed models could be considered as valuable ols for the process planning of MEDD of blind micro holes in industry. Variables X1:Depth of hole X2:Capacitance X3:Gap voltage R a : 2D surface finish, S a : 3D surface finish, L : Electrode wear length Figure 5Overlaid zone of machining parameters for selected range of responses : (a) (c) for R a and L & (d) (e) for S a and L Table 7 Optimizedzoneof Gap Voltage, Capacitance and Depth of hole for selected range of R a, S a and electrode wear length L Selected range of responses R a (µm) L (mm) S a (µm) L (mm) Conclusions Gap Voltage(V) Optimized zone Capacitance (nf) Depth of hole (mm) Twenty seven blind micro holes of different hole depth were drilled on Monel K-500 by tungsten electrode of 300 µm diameter with combinations of varying machining parameters gap voltage, capacitance and depth of hole study the influence of machining iii) Conur plots for R a, S a and L were plotted against machining parameters gap voltage, capacitance and depth of blind micro holes. The optimum parametric settings for the machining parameters were computed from overlaid conur plots of responses. iv) For machining a surface of R a of 6.2 µm 6.6 µm, S a of 17 µm 17.5 µm for MonelK-500 alloy and with electrode wear length of 150 µm 180 µm for tungsten electrode of 300 µm diameter, the optimized zoneof machining parameters gap voltage, capacitance and depth of hole were computed. Acknowledgement Authors are thankful Department of Science and Technology, Govt. of India (SERB Grant no. SR/S3/MERC/0020/2012 dated ) for financial support the project on micro EDM and also thankful Direcr, CSIR-Central Mechanical Engineering Research Institute, Durgapur for kind permission submit the paper AIMTDR References Alting, I., Kimourta, F., Hansen, H.N. and Bissacco, G. (2003), Micro engineering, Annals CIRP Manufacturing Technology, Vol. 52(2), pp Masuzawa, T. (2000), State of art of micromachining, Annals CIRP Manufacturing Technology, Vol. 49(2), pp Montgomery, D.C.(2000), Design and analysis of experiments, John Wiley & Sons, 5th Edition Myers R.H., Montgomery, D.C. and Anderson-Cook, C.M. (2009), Response surface methodology : Process and product optimization using designed experiments, 809-6

7 John Wiley & Sons, 3 rd Edition Pham, D.T., Dimov, S.S., Bigot, A. and Popov K.(2004),Micro-EDM recent developments and research issues, Journal of Material Processing Technology, Vol.149, pp Puertas, I., Luis, C.J. and Alvarez, I. (2004), Analysis of the influence of EDM parameter on surface quality, MRR and EW of WC-Co, Journal of Material Processing Technology, Vol. 153/154 pp

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