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Experimental study on micro machining of SS304 by using electric discharge machining
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 5 (2018) 27269–27276
www.materialstoday.com/proceedings
ICAMM_2016
Experimental study on micro machining of SS304 by using electric discharge machining M. Ravi Kumara* A. Krishnaiahb and Ravi Shankar Kalvac a
P.G. Student M.E., Dept. of Mech Engg., Univ.College of Engg, Osmania University, Hyd, TS – 500 007, India Professor & Head, Dept. of Mech Engg., Univ.College of Engg, Osmania University, Hyd, TS – 500 007, India c Professor & H.O.D, Dept. of Mech Engg., J.B.R.E.C., Hyderabad, Telangana- 500 075, India ____________________________________________________________________________________________________________________ b
Abstract Electric discharge machining is an electro thermal non traditional machining process where electrical energy is used to generate electrical spark and material removal occurs mainly due to thermal energy of the spark. EDM is used to machine the austenite stainless steel (304,316,302), Ferrite S.S. (430, 444, Cr12), Titanium, Copper, Copper alloy, Tool steel, Nickel, Chromium alloy. SS 304 is the basic grade of stainless steel. It is having Good corrosion resistance, electrical conductivity and very good mechanical strength and creep resistance. It is generally used in Pipe and heat exchanger tubes for chemical and petrochemical industries and for boilers. In the present work deals with the central-composition design (CCD)-RSM of three factors with five levels was used to design the experiments. The experiments carried out on die sinker EDM. The experiments were carried out to study the effect of process parameters such as current, pulse on time and pulse off time on responses such as MRR, TWR and Circularity on SS 304. In this paper an attempt has made to develop mathematical model for relating MRR, TWR and circularity to the input process parameters. Experimental results shows Current are the most significant factor for analyzing MRR and TWR. Pulse off time most significant factor for circularity Keywords: Micro machining, Stainless Steel, Electric Discharge Machining ____________________________________________________________________________________________________________________
1. Introduction Conventional machining was fulfilling the requirement of industries over the decades. But newer work material and innovative geometric design of products and components have brought lot of pressure on capabilities of conventional machining processes to manufacture the components with desired tolerances economically. This led to the development and establishment of Non Traditional Machining (NTM) processes in industries as efficient and economic alternatives to conventional ones. Electric Discharge Machining (EDM) is an electro-thermal nontraditional machining process, where electrical energy is used to generate electrical spark and material removal mainly occurs due to thermal energy of the spark. EDM is mainly used to machine difficult- to-machine materials and high strength temperature resistant alloys. EDM can be used to machine difficult geometries in small batches or even on job-shop basis. Work material to be machined by EDM has to be electrically conductive. 2. Materials and methodology 2.1 Work Material The work material chosen for this experimental work is SS 304 (stainless steel). The work piece is a rectangular bar of 150x25x2 mm3. The work piece is machined with surface grinding to ensure the uniform thickness. _________ Corresponding author: e-mail: [email protected]
2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016.
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Element Max %
C 0.08
Cr 18-20
Table. 1 Composition of SS 304 Steel Ni Fe Mn 8-10.5 66.3-74 2
P 0.05
S 0.03
Si 1
2.2 Electrode material The electrode used was made of Electrolytic Tough pitch Copper. Initially ø 5 mm Copper (Cu-ETP grade ) rod is purchased and machined to ø 800 Microns for the length of 25 mm by Cylindrical grinding machine. The machined copper piece used as the Electrode for this process. The chemical composition of ETP electrode known through wet analysis method and their values are given below as per table 2.
CU-ETP
Element Max %
Table. 2 Chemical composition of ETP Electrode cu Pb Sn Fe 99.55 0.015 0.26 0.001
Bi 0.001
Ag 0.013
2.3 Response surface methodology Response surface methodology (RSM) is used to analyze problem where there are several variables influencing the response and the purpose of the experiments is to optimize the responses. In design of experiment, the aim is to design an experiment and draw conclusion about the factor which are effect the response variable. The value of the response variable is a function of the level of the factors which are considered in the experiment, for a particular factor, the value of the treatment of the factor will form a lower limit to an upper limit. So the investigator may design a regression model to estimate the effect of the level of the factor on the response variable. Regression is the dependence of a variable on one or more other variable. Consider the following linear equation which is known as linear regression equation. y= a + bx where 'y' is the dependent variable, 'x' is the intercept, and 'a' is the intercept and 'b' is the slope. 2.4 Experimental procedure The experiments are carried out with ACRO ZNC S-430 Electrical Discharge Machine. The work piece is clamped on the machine and the uniformity of the surface is checked by a dial indicator. The copper electrode is then clamped in position. The datum is set by using a CNC controller. The electrode is positioned such that the machining starts from the right hand end of the rectangular bar. Experiments were conducted with positive polarity of electrode. Castrol Honilo 401 special light mineral oil blend is used as a dielectric fluid. experimental set up as shown in Fig. 1.
Fig. 1. Setup for ACRO ZNC S430 EDM Experiments
Input process parameters play a very significant role in determining the responses like Material removal rate, Electrode wear rate, Circularity. From the Literature review, Process parameters like Current (A), Pulse on (µs) Pulse off (µs) were found to be most important factors. In this experiment three factors and five levels of values were selected for the experiments. The experimental study was carried out based on the CCD –RSM. The selected process parameter with range is tabulated in Table No.3.
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Table. 3 Process Parameters used in the Experiment Factors Current - A Pulse on (T-on)- B Pulse off (T-off) - C
Unit A µs µs
-1.682 0.64 2.64 4.64
-1 2 4 6
Levels 0 4 6 8
1 6 8 10
1.682 7.68 9.36 11.68
2.5 Evaluation of performance characteristics Experiments were conducted as per the RSM. For Experiments No.1, Current , Pulse on and Pulse off were taken as 2 A, 4 µs and 6 µs respectively. Uniform depth 2 mm was machined in all experiments and machining time was recorded upon each completion of machining. The performance characteristics i.e., Metal Removal Rate (MRR), Tool Wear Rate (TWR) and circularity error are measured as follows. 2.6 Evaluation of material removal rate (MRR) The Material removal rate (MRR) can be calculated by using the volume of the material removed (V) and machining time (t). Testing And Evaluation Tool Wear Rate (TWR): The Volumetric Tool Wear Rate can be calculated as follows: Tool Wear Rate (TWR) =
;
w1– weight of Electrode before machining (g), w2– weight of Electrode after machining (g), t - Machining Time (min).The weight of electrode before and after machining will be measured with help of weighing machine 2.7 Testing and evaluation circularity error Circularity is a form control. The circularity control defines how much the cross-sections of a surface of revolution on a real part may vary from an ideal circle. Circularity will be measured with help of video profile projector. The video profile projector especially designed to facilitate the fast and accurate measurement of mechanical parts, plastic injection parts, electronics devices, dies, and mould. 2.8 Circularity error is measured as follows First the reference circle profile is drawn by using master piece. The master piece consists of 200 microns dia hole to 20 mm dia. 800 microns master piece is selected and profile is captured by using video profile projector as shown in Fig. 2. The resolution and magnification is set. Then place work piece on the work-table. The image is captured and exported to the computer through the camera, then by using software functions together with work-table moving, the coordinate noted and measurements from the profile of the work piece. The same procedure is repeated for the balance experiments. The Circularity error is measured by using radial separation method. It is the difference of the diameters of the minimum circumscribing circle (MCC) and maximum inscribing circle (MIC). Circularity Error: diameter of MCC- diameter of MIC = 855-796 = 59 microns.
Figure. 2 Circularity measured with help of video profile projector.
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Table. 4 Experimental Results for MRR, TWR AND CIRCULARITY ERROR Factor Factor Material Factor 01 Tool Wear Circularity 02 03 removal Standard Rate. TWR Error. rate. MRR Order Pulse On Pulse (mm3/min) (microns) Current(A) (mm3/min) (µs) Off (µs) 1 2 4 6 0.075 0.167 60 2 6 4 6 0.194 0.326 59 3 2 8 6 0.07 0.241 83 4 6 8 6 0.218 0.585 65 5 2 4 10 0.075 0.227 57 6 6 4 10 0.193 0.862 17 7 2 8 10 0.091 0.152 73 8 6 8 10 0.253 1.243 47 9 0.64 6 8 0.023 0.149 48 10 7.36 6 8 0.287 0.803 43 11 4 2.64 8 0.132 0.768 38 12 4 9.36 8 0.174 0.449 42 13 4 6 4.64 0.136 0.99 83 14 4 6 11.36 0.136 0.259 44 15 4 6 8 0.121 0.272 79 16 4 6 8 0.109 0.342 80 17 4 6 8 0.121 0.541 39 18 4 6 8 0.118 0.212 64 19 4 6 8 0.124 0.167 51 20 4 6 8 0.1242 0.514 73
3. Results and Discussion Based on the statistical analysis, the model fits the experimental data; model was adequate, no significant lack of fits. The satisfactory R squared values of MRR, TWR, and circularity (0.9907, 0.5457, 0.4222) respectively. The effect of input process parameters of Die sinker Edm such a current, pulse on and pulse off was investigated using a CCD on Various output parameters presented in table 4, using a random order of experimental runs. The analysis of variance (ANOVA) test, F value and P-Values for MRR, mentioned in table no 5. Initially the terms current (A), Pulse on(B), Pulse off(c), AB, AC, BC, A2, B2, C2 were included in the response surface model and are statistically significant. The ANOVA indicates that quadratic model of MRR is statistically significant but lack of fit is not. By using backward elimination process the insignificant model terms are eliminated and reduced model is shown in Table 5. (R-Squared, Adjusted R Squared Values of the Model are 99.04%, 98.35 % respectively). Table. 5 Analysis Of Variance (ANOVA) For MRR
Source
Sum of squares
DOF
Mean Square
F value
p-value Prob > F
Model A-Current B-Pulse on C-pulse off AB BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total
0.079 0.072 1.99E-03 2.22E-04 6.74E-04 4.03E-04 1.99E-03 1.74E-03 3.56E-04 7.59E-04 5.98E-04 1.61E-04 0.079
8 1 1 1 1 1 1 1 1 11 6 5 19
9.83E-03 0.072 1.99E-03 2.22E-04 6.74E-04 4.03E-04 1.99E-03 1.74E-03 3.56E-04 6.90E-05 9.97E-05 3.22E-05
142.45 1041.06 28.85 3.21 9.77 5.84 28.77 25.15 5.16
< 0.0001 < 0.0001 0.0002 0.1007 0.0097 0.0342 0.0002 0.0004 0.0443
Significant
3.1
0.1176
Not significant
Remarks
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According to the analysis of variance (ANOVA) test, F value and P-Values for TWR, mentioned in table 5. Initially the terms current (A), Pulse on (B), Pulse off (c), AB, AC, BC, A2, B2, C2 were included in the response surface model and are statistically significant. The ANOVA indicates that linear model of TWR is statistically significant but lack of fit is not. By using backward elimination process the insignificant model terms are eliminated and reduced model is shown in Table 6 (R-Squared, Adjusted R Squared Values of the Model are 54.57 %, 52.02% respectively). Table. 6 Analysis of Variance (ANOVA) for TWR p-value
Sum of Squares
DOF
Mean Square
F Value
Model
3.18
1
3.19
21.62
0.0002
A-Current
3.18
1
3.18
21.62
0.0002
Residual
0.15 0.719
0.7142
Source
2.64
18
Lack of Fit
1.71
13
0.13
Pure Error
0.93
5
0.19
Cor Total
5.047
19
Prob > F
Remarks significant
not significant
According to the analysis of variance (ANOVA) test, F value and P-Values for TWR, mentioned in table 7 Initially the terms current (A), Pulse on (B), Pulse off (c), AB, AC, BC, A2, B2, C2 were included in the response surface model and are statistically significant. The ANOVA indicates that linear model of Circularity is statistically significant but lack of fit is not. By using backward elimination process the insignificant model terms are eliminated and reduced model is shown in Table 7. (R-Squared, Adjusted R Squared Values of the Model Are 42.22 %, 35.00% respectively.)
Source Model A-Current C-pulse off Residual Lack of Fit Pure Error Cor Total
Sum of Squares 0.0025 0.0011 0.0014 0.0035 0.0021 0.0014 0.006
Table. 7 Analysis of Variance (ANOVA) for Circularity Error p-value DOF Mean Square F Value Prob > F 2 0.0013 5.8465 0.0124 1 0.0011 5.2057 0.0365 1 0.0014 6.4873 0.0215 16 0.0002 11 0.0002 0.7088 0.7061 5 0.0003 18
Remarks significant
not significant
All three input process parameters affecting the quality of micro hole. The developed mathematical models are used to predict the MRR, TWR and Circularity by using design expert software with a actual form as follows. Regression model equation for MRR = 0.12 + 0.073 * current + 0.012 * Pulse on + 0.004 Pulse off + 0.009 current *Pulse on + 0.007 Pulse on *Pulse off + 0.012 current2 + 0.011 Pulse on2+0.005 Pulse off 2. Regression model equation for TWR = 1.0/Sqrt (TWR) =1.7209-0.4739 Current. Regression model equation of circularity error = 0.059-0.0102 current-0.0101Pulse off. 3.1 Analysis of MRR From the analysis of ANOVA (after dropping the insignificant factors) current and pulse on time was the most significant factor for achieving the maximum material removal rate. With the increase of current, the spark energy and surface temperature of work piece rises, and material melting and MRR increase rapidly. Spark energy intensifies with the increase of current, pulse on time and voltage. The increase of current increases the energy and number of positive ions attacking the surface of the work piece and causes the temperature of the work piece to rise, thereby melting and evaporating material from the work piece surface thus increasing the MRR with the increase of pulse on time, the plasma channel becomes wider and positive ions become more active in attacking the work piece.
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This causes more melting and evaporation of the work piece, and leads to the increase of MRR. There is no significant effect of pulse off on MRR. Maximum material removal rate achieved with the current (6 A), pulse on (8 µs) and pulse off (10 µs). Figure 3 shows the relation between current Vs pulse on & pulse on Vs pulse off for maximum MRR.
Figure. 3 Shows relation between current Vs pulse on & pulse on Vs pulse off for maximum MRR
3.2 Analysis of TWR From the analysis of ANOVA (After dropping the insignificant factors) current was the most significant factor for achieving the minimum Tool Wear rate. Minimum Tool wear rate achieved with the current (2A), pulse on (8 µs) and pulse off (8 µs). 3.3 Analysis of Circularity Error From the analysis of ANOVA (After dropping the insignificant factors) current and Pulse off time was the most significant factor for achieving the minimum circularity error. Minimum circularity error achieved with the current (6A), pulse on (8 µs) and pulse off (10 µs). Multi Optimization: After optimizing the individual targets, it was optimized three input process parameters together to get the desirable parameters for experiments. After that conducted the experiments and calculated the experimental value of MRR, TWR, and circularity error and compared with responsive surface methodology values, calculated the percentage of error and reported in fig. 4 and fig.5. Figure 6 shows the photograph of SS304 after machining micro holes according to optimized parameters.
Figure. 4 Shows relation between current and Pulse on and Desirability.
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Figure. 5 Shows relation between current and Pulse on time
Figure. 6 Photograph shows the micro holes on SS304 Table. 8 Experimental Results Factor 1 Current(A) 4.94 4.94 4.94
Factor 2 Pulse On (µs) 8 8 8
Machining Time Pulse Off (µs) In Minutes 10 6.55 10 6.69 10 6.49 Average Experimental Values Predicted Values Error Factor3
MRR
TWR
Circularity Error
mm3/min
mm3/min
0.154 0.15 0.155 0.153 0.199 0.046
0.309 0.359 0.329 0.331 0.445 -0.114
µm 38 40 37 38 43 5
4. Conclusions In this work the following observations were made as follows. The higher value of Material Removal Rate is achieved in the optimal combination of current value of 6 A, pulse-on time of 8 µs and a pulse-off time of 10 µs. Current and pulse on most influencing factor in deciding Maximum Material Removal Rate.
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The lower value of Tool wear rate is achieved in the optimal combination of current value of 2 A, pulse-on time of 8 µs and a pulse-off time of 8 µs. Current found to be most influencing factor in deciding Electrode wear rate. The lower value of circularity is achieved in the optimal combination of current value of 6 A, pulse-on time of 8 µs and a pulse-off time of 10 µs. Pulse off time & current found to be most influencing factor in deciding circularity. Current value of 5 A, pulse-on time of 8 µs and a pulse-off time of 10 µs is the optimal solution for multi objective of maximum MRR, Minimum TWR and Minimum Circularity Error.
Acknowledgements: This work was carried out as part of a major research project sponsored by UGC, Government of India and this assistance is gratefully acknowledged. References [1] Vijay Kumar Meena & Man Singh Azad , “Grey Relational Analysis of Micro-EDM Machining of Ti-6Al-4V Alloy” , Materials and Manufacturing Processes, 27:9, 973-977, DOI: 10.1080/10426914.2011.610080. [2] Palani Sivaprakasam, P. Hariharan & S. Gowri “Optimization of Micro-WEDM Process of Aluminum Matrix Composite (A413-B4C): A Response Surface Approach”, Materials and Manufacturing Processes, 28:12, 1340-1347, DOI: 10.1080/10426914.2013.823502, 2013 [3] T. Ponel Murugan & T.Rajasekaran “Experimental and Investigation of Micro Electric discharge Machining Process of AISI 1040” Arpn Journal of Engineering and Applied Sciences Vol. 10, No. 6, April 2015.
ScienceDirect Materials Today: Proceedings 5 (2018) 27269–27276
www.materialstoday.com/proceedings
ICAMM_2016
Experimental study on micro machining of SS304 by using electric discharge machining M. Ravi Kumara* A. Krishnaiahb and Ravi Shankar Kalvac a
P.G. Student M.E., Dept. of Mech Engg., Univ.College of Engg, Osmania University, Hyd, TS – 500 007, India Professor & Head, Dept. of Mech Engg., Univ.College of Engg, Osmania University, Hyd, TS – 500 007, India c Professor & H.O.D, Dept. of Mech Engg., J.B.R.E.C., Hyderabad, Telangana- 500 075, India ____________________________________________________________________________________________________________________ b
Abstract Electric discharge machining is an electro thermal non traditional machining process where electrical energy is used to generate electrical spark and material removal occurs mainly due to thermal energy of the spark. EDM is used to machine the austenite stainless steel (304,316,302), Ferrite S.S. (430, 444, Cr12), Titanium, Copper, Copper alloy, Tool steel, Nickel, Chromium alloy. SS 304 is the basic grade of stainless steel. It is having Good corrosion resistance, electrical conductivity and very good mechanical strength and creep resistance. It is generally used in Pipe and heat exchanger tubes for chemical and petrochemical industries and for boilers. In the present work deals with the central-composition design (CCD)-RSM of three factors with five levels was used to design the experiments. The experiments carried out on die sinker EDM. The experiments were carried out to study the effect of process parameters such as current, pulse on time and pulse off time on responses such as MRR, TWR and Circularity on SS 304. In this paper an attempt has made to develop mathematical model for relating MRR, TWR and circularity to the input process parameters. Experimental results shows Current are the most significant factor for analyzing MRR and TWR. Pulse off time most significant factor for circularity Keywords: Micro machining, Stainless Steel, Electric Discharge Machining ____________________________________________________________________________________________________________________
1. Introduction Conventional machining was fulfilling the requirement of industries over the decades. But newer work material and innovative geometric design of products and components have brought lot of pressure on capabilities of conventional machining processes to manufacture the components with desired tolerances economically. This led to the development and establishment of Non Traditional Machining (NTM) processes in industries as efficient and economic alternatives to conventional ones. Electric Discharge Machining (EDM) is an electro-thermal nontraditional machining process, where electrical energy is used to generate electrical spark and material removal mainly occurs due to thermal energy of the spark. EDM is mainly used to machine difficult- to-machine materials and high strength temperature resistant alloys. EDM can be used to machine difficult geometries in small batches or even on job-shop basis. Work material to be machined by EDM has to be electrically conductive. 2. Materials and methodology 2.1 Work Material The work material chosen for this experimental work is SS 304 (stainless steel). The work piece is a rectangular bar of 150x25x2 mm3. The work piece is machined with surface grinding to ensure the uniform thickness. _________ Corresponding author: e-mail: [email protected]
2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016.
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Element Max %
C 0.08
Cr 18-20
Table. 1 Composition of SS 304 Steel Ni Fe Mn 8-10.5 66.3-74 2
P 0.05
S 0.03
Si 1
2.2 Electrode material The electrode used was made of Electrolytic Tough pitch Copper. Initially ø 5 mm Copper (Cu-ETP grade ) rod is purchased and machined to ø 800 Microns for the length of 25 mm by Cylindrical grinding machine. The machined copper piece used as the Electrode for this process. The chemical composition of ETP electrode known through wet analysis method and their values are given below as per table 2.
CU-ETP
Element Max %
Table. 2 Chemical composition of ETP Electrode cu Pb Sn Fe 99.55 0.015 0.26 0.001
Bi 0.001
Ag 0.013
2.3 Response surface methodology Response surface methodology (RSM) is used to analyze problem where there are several variables influencing the response and the purpose of the experiments is to optimize the responses. In design of experiment, the aim is to design an experiment and draw conclusion about the factor which are effect the response variable. The value of the response variable is a function of the level of the factors which are considered in the experiment, for a particular factor, the value of the treatment of the factor will form a lower limit to an upper limit. So the investigator may design a regression model to estimate the effect of the level of the factor on the response variable. Regression is the dependence of a variable on one or more other variable. Consider the following linear equation which is known as linear regression equation. y= a + bx where 'y' is the dependent variable, 'x' is the intercept, and 'a' is the intercept and 'b' is the slope. 2.4 Experimental procedure The experiments are carried out with ACRO ZNC S-430 Electrical Discharge Machine. The work piece is clamped on the machine and the uniformity of the surface is checked by a dial indicator. The copper electrode is then clamped in position. The datum is set by using a CNC controller. The electrode is positioned such that the machining starts from the right hand end of the rectangular bar. Experiments were conducted with positive polarity of electrode. Castrol Honilo 401 special light mineral oil blend is used as a dielectric fluid. experimental set up as shown in Fig. 1.
Fig. 1. Setup for ACRO ZNC S430 EDM Experiments
Input process parameters play a very significant role in determining the responses like Material removal rate, Electrode wear rate, Circularity. From the Literature review, Process parameters like Current (A), Pulse on (µs) Pulse off (µs) were found to be most important factors. In this experiment three factors and five levels of values were selected for the experiments. The experimental study was carried out based on the CCD –RSM. The selected process parameter with range is tabulated in Table No.3.
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Table. 3 Process Parameters used in the Experiment Factors Current - A Pulse on (T-on)- B Pulse off (T-off) - C
Unit A µs µs
-1.682 0.64 2.64 4.64
-1 2 4 6
Levels 0 4 6 8
1 6 8 10
1.682 7.68 9.36 11.68
2.5 Evaluation of performance characteristics Experiments were conducted as per the RSM. For Experiments No.1, Current , Pulse on and Pulse off were taken as 2 A, 4 µs and 6 µs respectively. Uniform depth 2 mm was machined in all experiments and machining time was recorded upon each completion of machining. The performance characteristics i.e., Metal Removal Rate (MRR), Tool Wear Rate (TWR) and circularity error are measured as follows. 2.6 Evaluation of material removal rate (MRR) The Material removal rate (MRR) can be calculated by using the volume of the material removed (V) and machining time (t). Testing And Evaluation Tool Wear Rate (TWR): The Volumetric Tool Wear Rate can be calculated as follows: Tool Wear Rate (TWR) =
;
w1– weight of Electrode before machining (g), w2– weight of Electrode after machining (g), t - Machining Time (min).The weight of electrode before and after machining will be measured with help of weighing machine 2.7 Testing and evaluation circularity error Circularity is a form control. The circularity control defines how much the cross-sections of a surface of revolution on a real part may vary from an ideal circle. Circularity will be measured with help of video profile projector. The video profile projector especially designed to facilitate the fast and accurate measurement of mechanical parts, plastic injection parts, electronics devices, dies, and mould. 2.8 Circularity error is measured as follows First the reference circle profile is drawn by using master piece. The master piece consists of 200 microns dia hole to 20 mm dia. 800 microns master piece is selected and profile is captured by using video profile projector as shown in Fig. 2. The resolution and magnification is set. Then place work piece on the work-table. The image is captured and exported to the computer through the camera, then by using software functions together with work-table moving, the coordinate noted and measurements from the profile of the work piece. The same procedure is repeated for the balance experiments. The Circularity error is measured by using radial separation method. It is the difference of the diameters of the minimum circumscribing circle (MCC) and maximum inscribing circle (MIC). Circularity Error: diameter of MCC- diameter of MIC = 855-796 = 59 microns.
Figure. 2 Circularity measured with help of video profile projector.
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Table. 4 Experimental Results for MRR, TWR AND CIRCULARITY ERROR Factor Factor Material Factor 01 Tool Wear Circularity 02 03 removal Standard Rate. TWR Error. rate. MRR Order Pulse On Pulse (mm3/min) (microns) Current(A) (mm3/min) (µs) Off (µs) 1 2 4 6 0.075 0.167 60 2 6 4 6 0.194 0.326 59 3 2 8 6 0.07 0.241 83 4 6 8 6 0.218 0.585 65 5 2 4 10 0.075 0.227 57 6 6 4 10 0.193 0.862 17 7 2 8 10 0.091 0.152 73 8 6 8 10 0.253 1.243 47 9 0.64 6 8 0.023 0.149 48 10 7.36 6 8 0.287 0.803 43 11 4 2.64 8 0.132 0.768 38 12 4 9.36 8 0.174 0.449 42 13 4 6 4.64 0.136 0.99 83 14 4 6 11.36 0.136 0.259 44 15 4 6 8 0.121 0.272 79 16 4 6 8 0.109 0.342 80 17 4 6 8 0.121 0.541 39 18 4 6 8 0.118 0.212 64 19 4 6 8 0.124 0.167 51 20 4 6 8 0.1242 0.514 73
3. Results and Discussion Based on the statistical analysis, the model fits the experimental data; model was adequate, no significant lack of fits. The satisfactory R squared values of MRR, TWR, and circularity (0.9907, 0.5457, 0.4222) respectively. The effect of input process parameters of Die sinker Edm such a current, pulse on and pulse off was investigated using a CCD on Various output parameters presented in table 4, using a random order of experimental runs. The analysis of variance (ANOVA) test, F value and P-Values for MRR, mentioned in table no 5. Initially the terms current (A), Pulse on(B), Pulse off(c), AB, AC, BC, A2, B2, C2 were included in the response surface model and are statistically significant. The ANOVA indicates that quadratic model of MRR is statistically significant but lack of fit is not. By using backward elimination process the insignificant model terms are eliminated and reduced model is shown in Table 5. (R-Squared, Adjusted R Squared Values of the Model are 99.04%, 98.35 % respectively). Table. 5 Analysis Of Variance (ANOVA) For MRR
Source
Sum of squares
DOF
Mean Square
F value
p-value Prob > F
Model A-Current B-Pulse on C-pulse off AB BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total
0.079 0.072 1.99E-03 2.22E-04 6.74E-04 4.03E-04 1.99E-03 1.74E-03 3.56E-04 7.59E-04 5.98E-04 1.61E-04 0.079
8 1 1 1 1 1 1 1 1 11 6 5 19
9.83E-03 0.072 1.99E-03 2.22E-04 6.74E-04 4.03E-04 1.99E-03 1.74E-03 3.56E-04 6.90E-05 9.97E-05 3.22E-05
142.45 1041.06 28.85 3.21 9.77 5.84 28.77 25.15 5.16
< 0.0001 < 0.0001 0.0002 0.1007 0.0097 0.0342 0.0002 0.0004 0.0443
Significant
3.1
0.1176
Not significant
Remarks
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According to the analysis of variance (ANOVA) test, F value and P-Values for TWR, mentioned in table 5. Initially the terms current (A), Pulse on (B), Pulse off (c), AB, AC, BC, A2, B2, C2 were included in the response surface model and are statistically significant. The ANOVA indicates that linear model of TWR is statistically significant but lack of fit is not. By using backward elimination process the insignificant model terms are eliminated and reduced model is shown in Table 6 (R-Squared, Adjusted R Squared Values of the Model are 54.57 %, 52.02% respectively). Table. 6 Analysis of Variance (ANOVA) for TWR p-value
Sum of Squares
DOF
Mean Square
F Value
Model
3.18
1
3.19
21.62
0.0002
A-Current
3.18
1
3.18
21.62
0.0002
Residual
0.15 0.719
0.7142
Source
2.64
18
Lack of Fit
1.71
13
0.13
Pure Error
0.93
5
0.19
Cor Total
5.047
19
Prob > F
Remarks significant
not significant
According to the analysis of variance (ANOVA) test, F value and P-Values for TWR, mentioned in table 7 Initially the terms current (A), Pulse on (B), Pulse off (c), AB, AC, BC, A2, B2, C2 were included in the response surface model and are statistically significant. The ANOVA indicates that linear model of Circularity is statistically significant but lack of fit is not. By using backward elimination process the insignificant model terms are eliminated and reduced model is shown in Table 7. (R-Squared, Adjusted R Squared Values of the Model Are 42.22 %, 35.00% respectively.)
Source Model A-Current C-pulse off Residual Lack of Fit Pure Error Cor Total
Sum of Squares 0.0025 0.0011 0.0014 0.0035 0.0021 0.0014 0.006
Table. 7 Analysis of Variance (ANOVA) for Circularity Error p-value DOF Mean Square F Value Prob > F 2 0.0013 5.8465 0.0124 1 0.0011 5.2057 0.0365 1 0.0014 6.4873 0.0215 16 0.0002 11 0.0002 0.7088 0.7061 5 0.0003 18
Remarks significant
not significant
All three input process parameters affecting the quality of micro hole. The developed mathematical models are used to predict the MRR, TWR and Circularity by using design expert software with a actual form as follows. Regression model equation for MRR = 0.12 + 0.073 * current + 0.012 * Pulse on + 0.004 Pulse off + 0.009 current *Pulse on + 0.007 Pulse on *Pulse off + 0.012 current2 + 0.011 Pulse on2+0.005 Pulse off 2. Regression model equation for TWR = 1.0/Sqrt (TWR) =1.7209-0.4739 Current. Regression model equation of circularity error = 0.059-0.0102 current-0.0101Pulse off. 3.1 Analysis of MRR From the analysis of ANOVA (after dropping the insignificant factors) current and pulse on time was the most significant factor for achieving the maximum material removal rate. With the increase of current, the spark energy and surface temperature of work piece rises, and material melting and MRR increase rapidly. Spark energy intensifies with the increase of current, pulse on time and voltage. The increase of current increases the energy and number of positive ions attacking the surface of the work piece and causes the temperature of the work piece to rise, thereby melting and evaporating material from the work piece surface thus increasing the MRR with the increase of pulse on time, the plasma channel becomes wider and positive ions become more active in attacking the work piece.
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This causes more melting and evaporation of the work piece, and leads to the increase of MRR. There is no significant effect of pulse off on MRR. Maximum material removal rate achieved with the current (6 A), pulse on (8 µs) and pulse off (10 µs). Figure 3 shows the relation between current Vs pulse on & pulse on Vs pulse off for maximum MRR.
Figure. 3 Shows relation between current Vs pulse on & pulse on Vs pulse off for maximum MRR
3.2 Analysis of TWR From the analysis of ANOVA (After dropping the insignificant factors) current was the most significant factor for achieving the minimum Tool Wear rate. Minimum Tool wear rate achieved with the current (2A), pulse on (8 µs) and pulse off (8 µs). 3.3 Analysis of Circularity Error From the analysis of ANOVA (After dropping the insignificant factors) current and Pulse off time was the most significant factor for achieving the minimum circularity error. Minimum circularity error achieved with the current (6A), pulse on (8 µs) and pulse off (10 µs). Multi Optimization: After optimizing the individual targets, it was optimized three input process parameters together to get the desirable parameters for experiments. After that conducted the experiments and calculated the experimental value of MRR, TWR, and circularity error and compared with responsive surface methodology values, calculated the percentage of error and reported in fig. 4 and fig.5. Figure 6 shows the photograph of SS304 after machining micro holes according to optimized parameters.
Figure. 4 Shows relation between current and Pulse on and Desirability.
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Figure. 5 Shows relation between current and Pulse on time
Figure. 6 Photograph shows the micro holes on SS304 Table. 8 Experimental Results Factor 1 Current(A) 4.94 4.94 4.94
Factor 2 Pulse On (µs) 8 8 8
Machining Time Pulse Off (µs) In Minutes 10 6.55 10 6.69 10 6.49 Average Experimental Values Predicted Values Error Factor3
MRR
TWR
Circularity Error
mm3/min
mm3/min
0.154 0.15 0.155 0.153 0.199 0.046
0.309 0.359 0.329 0.331 0.445 -0.114
µm 38 40 37 38 43 5
4. Conclusions In this work the following observations were made as follows. The higher value of Material Removal Rate is achieved in the optimal combination of current value of 6 A, pulse-on time of 8 µs and a pulse-off time of 10 µs. Current and pulse on most influencing factor in deciding Maximum Material Removal Rate.
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The lower value of Tool wear rate is achieved in the optimal combination of current value of 2 A, pulse-on time of 8 µs and a pulse-off time of 8 µs. Current found to be most influencing factor in deciding Electrode wear rate. The lower value of circularity is achieved in the optimal combination of current value of 6 A, pulse-on time of 8 µs and a pulse-off time of 10 µs. Pulse off time & current found to be most influencing factor in deciding circularity. Current value of 5 A, pulse-on time of 8 µs and a pulse-off time of 10 µs is the optimal solution for multi objective of maximum MRR, Minimum TWR and Minimum Circularity Error.
Acknowledgements: This work was carried out as part of a major research project sponsored by UGC, Government of India and this assistance is gratefully acknowledged. References [1] Vijay Kumar Meena & Man Singh Azad , “Grey Relational Analysis of Micro-EDM Machining of Ti-6Al-4V Alloy” , Materials and Manufacturing Processes, 27:9, 973-977, DOI: 10.1080/10426914.2011.610080. [2] Palani Sivaprakasam, P. Hariharan & S. Gowri “Optimization of Micro-WEDM Process of Aluminum Matrix Composite (A413-B4C): A Response Surface Approach”, Materials and Manufacturing Processes, 28:12, 1340-1347, DOI: 10.1080/10426914.2013.823502, 2013 [3] T. Ponel Murugan & T.Rajasekaran “Experimental and Investigation of Micro Electric discharge Machining Process of AISI 1040” Arpn Journal of Engineering and Applied Sciences Vol. 10, No. 6, April 2015.