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- 1. WELCOMEWELCOME Research Title - Investigation On Process Parameters Of Electric Discharge Machining Of Titanium Grade 5 Alloy By Mr. Saravanan Paramasivam / Head of Engineering Department / NCT / Oman Research Scholar under the Guidance of Dr. L. Antony Michael Raj / VP / SRM University – Ramapuram DC Members 1. Dr.G.Prabhakaran - Professor & VP / Velammal Engineering College 2. Dr.S.Satish Kumar - Professor / Department of Production / Velammal Engineering College 3. Dr.R.Varahamoorthi - Professor / Department of Manufacturing / Annamalai University 4. Dr.B.K.Vinayagam - Professor & Head / Department of Mechatronics / SRM University
- 2. ACKNOWLEDGEMENT Supervisor DC Members SRM Administration NCT Administration MoMP Friends & Well Wishers Family
- 3. Need ( Industrial) for Research Problem related to a popular aero industry material ASD-STAN PREN 2339 sheet, which is a cold rolled titanium grade 5 alloy sheet of thickness between 2mm and 3mm. Nearly 40% [12] of entire machining work done on titanium grade 5 alloy is to make holes on the sheets. So doing research in sink EDM is very useful to the industries at the same time thick sheets of titanium grade 5 alloys are cut by wire cut EDM before making holes. So researching both sink EDM & WEDM in the identified application specific area will be useful for the industries. Multi objective optimization of EDM parameters which results in the improvements of responses will be very useful for the industry to increase their overall productivity, so it always is a priority.
- 4. Titanium Alloys There are 38 grades of titanium alloys from pure to most alloyed, providing wide range of properties Out of all those alloys, titanium grade 5 alloy is most significant and is called “Aero Industry Work Horse”. Machining Titanium Grades 1, 2, 3, 4, 7 and 11 presents no particular difficulties while machining conventionally as long as chemical and physical characteristics are understood correctly but the other grades, particularly titanium grade 5 alloy is difficult to machine through conventional machining. The two machining procedures that are good for titanium grade 5 alloy are EDM & water jet machining [11][103, 104]
- 5. Titanium Grade 5 Alloy Chemical Composition ( Ti - 6Al - 4V by weight ) Properties • High tensile strength, high toughness, light weight, extraordinary corrosion resistance, having high hardness at high temperatures and great weld ability • It has a density of roughly 4420 kg/m3 , Young's modulus of 110 GPa, and tensile strength of 1000 MPa.[8] By comparison, annealed type 316 stainless steel has a density of 8000 kg/m3 , modulus of 193 GPa, and tensile strength of only 570 MPa.[9] And tempered 6061 aluminium alloy has 2700 kg/m3 , 69 GPa, and 310 MPa, respectively. Hardness 349 Vickers. • Highest strength-to-weight ratio metal. The titanium 6AL-4V alloy accounts for almost 50% of all alloys used in aircraft applications[31] Applications Military applications, aircraft, spacecraft, medical devices, connecting rods on expensive sports cars , premium sports equipment and medical implants.
- 6. About EDM
- 7. This process is much faster than electrode EDM. Wire EDM
- 8. Literature Survey – Critical # Year Author Title Work done Publisher 1 2010 Kuldeep Ojha, Garg, R. K., and Singh, K. K. MRR improvement in sinking electrical discharge machining : a review. A review paper analyzed over 500 published papers in EDM field and proposed probable research areas. J. Minerals & Mater. Characterization & Engineering, 2010, 9(8), 709-739. 2 2010 Anand P & Shankar Singh Current research trends in variants of EDM: A review International Journal of Engineering Science and Technology; 2(6) : 2172- 2191 3 2007 Ahmet Hascalik and Ulas Caydas. Electrical discharge machining of titanium alloy (Ti-6Al-4V) Metallurgical machinability study was done with SEM images of resultant surfaces. Applied Surface Science, 2007, 253, 9007-9016 4 2009 Pradhan,B.B ., and Bhattachary ya, B. Modeling of Micro-EDM during machining of Titanium Alloy , Ti-6Al-4V using RSM and ANN Algorithm Experiment modeling was done using RSM and ANN. Proc. IMechE Part B: J. Engineering Manufacture, 2009, 223, No. 6, 683-693. 5 2012 Belgassim,O., and Abusaada,A Investigation of the influence of EDM parameters on the machined surface hardness of AISI D3 tool steel. Influence of EDM process parameters were studied. Journal of Engineering Research, March 2012, 16, 31-38.
- 9. Problem Statement Aim of this research was to investigate the effect of pulse current, pulse on time, pulse off time and tool geometry on important EDM output responses such as metal removal rate (MRR), tool wear rate (TWR), surface roughness (SR) and entry exit deviation (EED) for three different tool electrode materials such as graphite, copper and brass while machining titanium grade 5 alloy using EDM. And also to investigate the effect of six critical wire cut EDM experimental parameters such as current, pulse on time, pulse off time, servo voltage , wire tension and wire speed on important WEDM output responses metal removal rate(MRR), surface roughness (SR) and wire offset while machining titanium grade 5 alloy using brass wire in wire cut EDM.
- 10. Research Objectives in EDM Experimental determination of the effects of the various process parameters on the performance measures ( Responses) in EDM process of titanium grade 5 alloy. Influence ranking of process parameters Modeling the performance measures using regression analysis and experimental validation. Finding electrical parameters interaction effect Multi-objective optimization of the process parameters of EDM process using statistical methods.
- 11. Research Objectives in WEDM Experimental determination of the effects of the various process parameters on the performance measures ( Responses) in WEDM process of titanium grade 5 alloy. Influence ranking of process parameters Modeling the performance measures using regression analysis and validation. Single response optimization of parameters using contour plots in WEDM. Multi-objective optimization of the process parameters of EDM process using SA, BFOA and CUCKOO evolutionary heuristic algorithms.
- 12. Stages of Research in EDM
- 13. Experiment Design - EDM Taguchi Design • L27 orthogonal array with interaction between factors • Four factors at three levels – Current – Pulse on time – Pulse off time – Tool cross sectional area and geometry • Held Constant Factors – Dielectric – Kerosene, Side Flushing, Pressure – 0.6MPa – Gap Voltage 60V Responses • MRR • TWR • Surface Roughness • Entry and exit deviation angle
- 14. Process Parameters & Levels for EDM PARAMETERS LEVELS L1 L2 L3 Current (I) amps 15 20 25 Pulse on-time (Ton)µs 50 100 200 Pulse off-time (Toff) µs 50 100 200 Tool electrode geometry (mm2 ) Rectangular (40) / Triangle (27.7) Circle (50.265) Square (64)
- 15. Experiment Design (Taguchi design using Minitab with the Consideration for Interaction Effects) Exp No Current ( amps) Pulse on time(µs) Pulse off time(µs) Geometry (sq mm) 1 1 1 1 3 2 1 1 2 1 3 1 1 3 2 4 1 2 1 1 5 1 2 2 2 6 1 3 1 2 7 1 2 3 3 8 1 3 2 3 9 1 3 3 1 10 2 1 1 1 11 2 1 2 2 12 2 1 3 3 13 2 2 1 2 14 2 2 2 3 15 2 2 3 1 16 2 3 1 3 17 2 3 2 1 18 2 3 3 2 19 3 1 1 2 20 3 1 2 3 21 3 1 3 1 22 3 2 1 3 23 3 2 2 1 24 3 2 3 2 25 3 3 1 1 26 3 3 2 2 27 3 3 3 3
- 16. Experimental Work ( Work Piece & Tool) Titanium Grade 5 Work piece Graphite Tools Used Copper Tools Used Brass Tools Used
- 17. Experimental Work Work piece & Electrode Setting EDM Machining in progress Machined Work Piece with Graphite Machined Work piece with Copper
- 18. Measurements CMM – Entry exit deviation measurement. MRR = MRW / (ρ x T) Where MRW is the work piece Metal Removal Weight in grams, ρ is the density of the work piece in gram per cubic millimeter and T is the machining time in minutes. TWR = TMLW / (ρ x T) Where TMLW is the Tool Material Lost Weight in grams, ρ is the density of the tool in gram per cubic millimeter and T is the machining time in minutes. Surface Roughness Measurement
- 19. Results Data - Graphite Tool Exp. No MRR (mm3 /min) TWR (mm3 /min) SR (µm) EED (º) 1 0.6642 0.0585 2.870 0.5548 2 0.7966 0.0625 2.601 0.5154 3 0.3914 0.0411 1.407 0.2146 4 0.8342 0.0638 2.729 0.4604 5 0.9646 0.0665 2.351 0.4085 6 0.5666 0.0537 1.819 0.2161 7 1.0646 0.0703 2.979 0.4510 8 1.1968 0.0819 2.346 0.2473 9 0.7964 0.0613 1.554 0.0725 10 0.7294 0.1285 2.794 0.7308 11 0.8558 0.1387 2.686 0.5121 12 0.4578 0.1197 1.639 0.3372 13 0.8954 0.1409 2.946 0.6054 14 1.0332 0.1444 2.417 0.5236 15 0.6356 0.1235 1.889 0.2129 16 1.1292 0.1492 3.205 0.4502 17 1.2636 0.1519 2.577 0.3919 18 0.8624 0.1384 1.634 0.1795 19 0.7886 0.2100 3.021 0.6766 20 0.9252 0.2086 2.648 0.6567 21 0.5472 0.1889 1.974 0.4643 22 0.9658 0.2109 3.276 0.7201 23 1.1000 0.2225 2.502 0.5023 24 0.6980 0.2019 1.868 0.3571 25 1.1964 0.2264 3.025 0.5883 26 1.3280 0.2308 2.913 0.5323 27 0.9330 0.2084 1.704 0.1822
- 20. ANOVA table for MRR (Graphite tool) Source DF Seq SS MS F P Current 2 9.708 4.8539 13.85 0.016 On Time 2 89.409 44.7043 127.58 0.003 Off Time 2 8.008 3.9522 10.62 0.028 Geometry 2 79.728 39.8642 113.76 0.025 Current* on time 4 0.731 0.1827 0.52 0.725 Current* off time 4 1.891 0.4728 1.35 0.353 On*Off 4 0.253 0.0633 0.18 0.94 Error 6 2.102 0.3504 Total 26 183.884
- 21. ANOVA table for TWR (Graphite tool) Source DF Seq SS MS F P Current 2 537.85 268.92 1036 0.001 On Time 2 11.71 5.855 22.55 0.002 Off Time 2 8.212 4.6 16.52 0.004 Geometry 2 9.615 4.808 18.52 0.003 Current* on ti 4 2.993 0.748 2.88 0.119 Current* off ti 4 0.179 0.045 0.17 0.945 On*Off 4 2.271 0.568 2.19 0.187 Error 6 1.558 0.26 Total 26 566.2
- 22. ANOVA table for SR (Graphite tool) Source DF Seq SS MS F P Current 2 4.22 2.11 18.06 0.003 On Time 2 110.92 55.458 474.6 0.001 Off Time 2 1.005 0.5025 4.3 0.049 Geometry 2 1.003 0.564 4.8 0.043 Current* on ti 4 0.301 0.0752 0.64 0.651 Current* off ti 4 5.429 1.3571 11.62 0.005 On*Off 4 0.334 0.0836 0.72 0.611 Error 6 0.701 0.1168 Total 26 123.02
- 23. ANOVA table for EED ( Graphite) DF Seq SS MS F P Current 2 79.361 39.681 13.2 0.006 On Time 2 334.94 167.47 55.71 0.001 Off Time 2 30.283 15.232 6.14 0.052 Geometry 2 96.321 48.161 16.02 0.004 Current* on ti 4 11.583 2.896 0.96 0.491 Current* off ti 4 15.698 3.924 1.31 0.366 On*Off 4 2.637 0.659 0.22 0.918 Error 6 18.038 3.006 Total 26
- 24. Residual Analysis in MRR (Graphite tool) Residual Percent 0.500.250.00-0.25-0.50 99 90 50 10 1 Fitt ed Value Residual 0-4-8 0.50 0.25 0.00 -0.25 -0.50 Residual Frequency 0.60.40.20.0-0.2-0.4 4.8 3.6 2.4 1.2 0.0 Obser vation Or der Residual 2624222018161412108642 0.50 0.25 0.00 -0.25 -0.50 Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a Residual Plot s f or S/ N r at ios - MRR Residual analysis plot for MRR – to check model adequacy
- 25. Residual Analysis in TWR (Graphite tool) Residual analysis plot for TWR – to check model adequacy Residual Percent 0.500.250.00-0.25-0.50 99 90 50 10 1 Fit t ed Value Residual -12-16-20-24-28 0.50 0.25 0.00 -0.25 -0.50 Residual Frequency 0.40.20.0-0.2-0.4 6.0 4.5 3.0 1.5 0.0 Obser vat ion Or der Residual 2624222018161412108642 0.50 0.25 0.00 -0.25 -0.50 Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a Residual Plots f or S/ N r at ios - TWR
- 26. Residual Analysis in SR (Graphite tool) Residual analysis plot for SR – to check model adequacy Residual Percent 0.40.20.0-0.2-0.4 99 90 50 10 1 Fitt ed Value Residual 10864 0.30 0.15 0.00 -0.15 -0.30 Residual Frequency 0.240.120.00-0.12-0.24 4.8 3.6 2.4 1.2 0.0 Obser vat ion Or der Residual 2624222018161412108642 0.30 0.15 0.00 -0.15 -0.30 Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a Residual Plot s f or S/ N r at ios - SR
- 27. Residual Analysis in EED (Graphite tool) Residual analysis plot for EED – to check model adequacy Residual Percent 210-1-2 99 90 50 10 1 Fitt ed Value Residual 0-5-10-15-20 1 0 -1 -2 Residual Frequency 1.51.00.50.0-0.5-1.0-1.5-2.0 6.0 4.5 3.0 1.5 0.0 Obser vat ion Or der Residual 2624222018161412108642 1 0 -1 -2 Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a Residual Plot s f or S/ N r at ios - EED
- 28. Findings - Factor effect on MRR (Graphite tool) MeanofS/Nratios 252015 0 -1 -2 -3 -4 20010050 20010050 0 -1 -2 -3 -4 64.0050.2640.00 Current Pulse on t ime Pulse off t ime Geomet ry Main Effects Plot (data means) for S/ N ratios - MRR Signal-to-noise: Larger is better Main effect plot for MRR Cur r ent 0 -2 -4 Pulse on t im e Pulse of f t im e 20010050 20010050 0 -2 -4 252015 0 -2 -4 Current 25 15 20 Pulse 200 on time 50 100 Pulse 200 off time 50 100 I nt er action Plot ( data means) f or S/ N r at ios - MRR Signal-to-noise: Larger is better Interaction effect plot for MRR
- 29. Findings - Factor effect on TWR (Graphite tool) Main effect plot for TWRInteraction effect plot for TWR MeanofS/Nratios 252015 -15.0 -17.5 -20.0 -22.5 -25.0 20010050 20010050 -15.0 -17.5 -20.0 -22.5 -25.0 64.0050.2640.00 Current Pulse on time Pulse off time Geometry Main Effects Plot (data means) for S/ N ratios - TWR Signal-to-noise: Larger is better Cur r ent -15 -20 -25 Pul se on t im e Pulse of f t im e 20010050 20010050 -15 -20 -25 252015 -15 -20 -25 Current 25 15 20 Pulse 200 on time 50 100 Pulse 200 off time 50 100 I nt er act ion Plot ( dat a means) f or S/ N r at ios - TWR Signal-to-noise: Larger is better
- 30. Findings - Factor effect on SR (Graphite tool) Main effect plot for SRInteraction effect plot for SR MeanofS/Nratios 252015 9 8 7 6 5 20010050 20010050 9 8 7 6 5 64.0050.2640.00 Current Pulse on time Pulse off time Geometry Main Effects Plot (data means) for S/ N ratios - SR Signal-to-noise: Larger is better Cur r ent 10.0 7.5 5.0 Pul se on t im e Pu lse of f t i m e 20010050 20010050 10.0 7.5 5.0 252015 10.0 7.5 5.0 Current 25 15 20 Pulse 200 on time 50 100 Pulse 200 off time 50 100 I nt er act ion Plot ( dat a means) f or S/ N r at ios - SR Signal-to-noise: Larger is better
- 31. Findings - Factor effect on EED (Graphite tool) Main effect plot for EEDInteraction effect plot for EED MeanofS/Nratios 252015 -4 -6 -8 -10 -12 20010050 20010050 -4 -6 -8 -10 -12 64.0050.2640.00 Current Pulse on time Pulse off time Geometry Main Effects Plot (data means) for S/ N ratios -EED Signal-to-noise: Larger is better Cur r e nt -5 -10 -15 Pu lse on t im e Pul se of f t im e 20010050 20010050 -5 -10 -15 252015 -5 -10 -15 Current 25 15 20 Pulse 200 on time 50 100 Pulse 200 off time 50 100 I nt er act ion Plot ( dat a means) f or S/ N r at ios - EED Signal-to-noise: Larger is better
- 32. Delta Values and Influence Ranking (Graphite Tool) Current Pulse on time Pulse off time Tool cross sectional area or geometry On MRR Delta 1.4687 4.3203 0.1165 4.2088 Rank 3 1 4 2 On TWR Delta 10.78 1.56 0.08 1.46 Rank 1 2 4 3 On SR Delta 0.968 4.808 0.425 0.158 Rank 2 1 3 4 On EED Delta 4.172 8.225 2.039 4.531 Rank 3 1 4 2
- 33. Regression Equations (Graphite Tool) In these equations W represents current in Amps, X represents pulse on time in µs, Y represents pulse off time in µs and Z represents tool cross sectional area in mm2 . MRR = - 0.009 + 0.0134 W - 0.00208 X - 0.000018 Y + 0.0167 Z R-Sq = 81.7% R-Sq(adj) = 78.4% TWR = - 0.190 + 0.0150 W - 0.000104 X + 0.000007 Y + 0.000749 Z R-Sq = 99.3% R-Sq(adj) = 99.2% SR = 2.89 + 0.0253 W - 0.00841 X- 0.000581 Y+ 0.00136 Z R-Sq = 94.5% R-Sq(adj) = 93.5% EED = 0.802 + 0.0171 W - 0.00223 X - 0.000637 Y - 0.00726 Z R-Sq = 99.5% R-Sq(adj) = 99.4% Their R2 values and adjusted R2 values are confirming the validity of the model as their values are above 95% for all the responses except MRR where it is close to 80% and is acceptable too .
- 34. Model Validation (Graphite Tool) Factors MRR TWR SR EED (mm3 /min) (mm3 /min) (µm) (º) 25A, 100µs, 100µs, circular Predicted 0.9555 0.2129 2.66 0.577 Exp. Result 0.9823 0.221 2.623 0.551 Variation 2.8% 3.8% 1.3% 4.5% 25A, 100µs, 100µs, square Predicted 1.185 0.2232 2.683 0.478 Exp. Result 1.152 0.2333 2.663 0.497 Variation 2.8% 4.5% 0.75% 4%
- 35. Results Data - Copper Tool Exp. No MRR (mm3 /min) TWR (mm3 /min) SR (µm) EED (º) 1 0.4982 0.0761 2.296 0.8322 2 0.5975 0.0813 2.0808 0.7731 3 0.2936 0.0534 1.1256 0.3219 4 0.6257 0.0829 2.1832 0.6906 5 0.7235 0.0865 1.8808 0.6128 6 0.425 0.0698 1.4552 0.3242 7 0.7985 0.0914 2.3832 0.6765 8 0.8976 0.1065 1.8768 0.371 9 0.5973 0.0797 1.2432 0.1088 10 0.5471 0.1671 2.2352 1.0962 11 0.6419 0.1803 2.1488 0.7682 12 0.3434 0.1556 1.3112 0.5058 13 0.6716 0.1832 2.3568 0.9081 14 0.7749 0.1877 1.9336 0.7854 15 0.4767 0.1606 1.5112 0.3194 16 0.8469 0.194 2.564 0.6753 17 0.9477 0.1975 2.0616 0.5879 18 0.6468 0.1799 1.3072 0.2693 19 0.5915 0.273 2.4168 1.0149 20 0.6939 0.2712 2.1184 0.9851 21 0.4104 0.2456 1.5792 0.6965 22 0.7244 0.2742 2.6208 1.0802 23 0.825 0.2893 2.0016 0.7535 24 0.5235 0.2625 1.4944 0.5357 25 0.8973 0.2943 2.42 0.8825 26 0.996 0.3 2.3304 0.7985 27 0.6998 0.2709 1.3632 0.2733
- 36. ANOVA table for MRR (Copper tool) Source DF Seq SS MS F P Current 2 13.680 6.840 14.04 0.005 On Time 2 87.559 43.77 89.89 0.00 Off Time 2 0.432 0.2158 0.44 0.06 Geometry 2 21.359 10.67 21.93 .002 Current* on time 4 2.815 0.7.39 1.45 0.327 Current* off time 4 0.667 0.1669 0.34 0.84 Current * Geometry 4 13.51 3.379 6.94 0.019 Error 6 2.922 0.487 Total 26 142.952
- 37. ANOVA table for TWR (Copper tool) Source DF Seq SS MS F P Current 2 376.05 188.02 660.61 0.000 On Time 2 13.991 6.995 24.58 0.001 Off Time 2 0.191 0.096 0.34 0.727 Geometry 2 150.744 75.372 264.81 0.00 Current* on time 4 2.427 0.607 2.13 0.194 Current* off time 4 0.555 0.139 0.49 0.746 Current * Geometry 4 51.878 12.969 45.57 0.00 Error 6 1.708 1.708 0.285 Total 26 597.544
- 38. ANOVA table for SR (Copper tool) Source DF Seq SS MS F P Current 2 2.639 1.31 3.94 0.08 On Time 2 125.699 62.84 187.73 0.00 Off Time 2 0.794 0.3970 1.19 0.038 Geometry 2 0.148 0.0739 0.22 0.08 Current* on time 4 0.940 0.235 0.70 0.618 Current* off time 4 3.44 0.860 2.57 0.145 Current * Geometry 4 1.910 0.4775 1.43 0.332 Error 6 2.009 0.3348 Total 26 137.579
- 39. ANOVA table for EED ( Copper tool ) Source DF Seq SS MS F P Current 2 98.507 49.254 12.02 0.008 On Time 2 447.152 223.576 54.58 0.00 Off Time 2 4.814 2.407 0.59 0.085 Geometry 2 0.913 0.456 0.11 0.06 Current* on time 4 18.898 4.725 1.15 0.416 Current* off time 4 20.235 5.059 1.23 0.388 Current * Geometry 4 114.062 28.515 6.96 0.019 Error 6 24.578 4.096 Total 26 729.159
- 40. Residual Analysis in MRR (Copper tool) Residual analysis plot for MRR – to check model adequacy
- 41. Residual Analysis in TWR (Copper tool) Residual analysis plot for TWR – to check model adequacy
- 42. Residual Analysis in SR (Copper tool) Residual analysis plot for SR – to check model adequacy
- 43. Residual Analysis in EED (Copper tool) Residual analysis plot for EED – to check model adequacy
- 44. Findings - Factor effect on MRR (Copper tool) Main effect plot for MRR Interaction effect plot for MRR
- 45. Findings - Factor effect on TWR (Copper tool) Main effect plot for TWRInteraction effect plot for TWR
- 46. Findings - Factor effect on SR (Copper tool) Main effect plot for SRInteraction effect plot for SR
- 47. Findings - Factor effect on EED (Copper tool) Main effect plot for EEDInteraction effect plot for EED
- 48. Delta Values and Influence Ranking (Copper Tool) Current Pulse on time Pulse off time Tool cross sectional area or geometry On MRR Delta 1.743 4.206 0.310 2.174 Rank 3 1 4 2 On Delta 8.41 1.75 0.20 5.79 TWR Rank 1 3 4 2 On SR Delta 0.675 5.154 0.415 0.170 Rank 2 1 3 4 On EED Delta 4.171 9.563 1.025 0.440 Rank 2 1 3 4
- 49. Regression Equations (Copper Tool) In these equations W represents current in Amps, X represents pulse on time in µs, Y represents pulse off time in µs and Z represents tool cross sectional area in mm2 . MRR = 0.283 + 0.0112 W - 0.00166 X+ 0.000023 Y + 0.00699 Z R-Sq = 87.0% R-Sq(adj) = 82.5% TWR = - 0.220 + 0.0196 W - 0.000154 X+ 0.000014 Y + 0.000447 Z R-Sq = 98.5% R-Sq(adj) = 98.2% SR = 2.51 + 0.0207 W- 0.00714 X- 0.000430 Y - 0.00232 Z R-Sq = 86.4% R-Sq(adj) = 83.9% EED = 1.04 + 0.0253 W - 0.00348 X - 0.000956 Y - 0.00787 Z R-Sq = 96.9% R-Sq(adj) = 96.3%
- 50. Model Validation (Copper Tool) Factors MRR TWR SR EED (mm3 /min) (mm3 /min) (µm) (º) 25A, 100µs, Predicted 0.6574 0.0992 1.8441 0.6433 100µs, circular Exp. Result 0.6891 0.1001 1.8512 0.5981 Variation 4.60% 0.90% 0.38% -7.56% 25A, 100µs, Predicted 0.7099 0.1036 1.8518 0.63552 100µs, square Exp. Result 0.7423 0.11 1.86 0.6456 Variation 4.36% 5.02% 0.44% 1.56%
- 51. Results Data – Brass Tool Exp. No MRR TWR SR EED (mm3 /min) (mm3 /min) (µm) (º) 1 0.79704 0.22815 4.1328 1.16508 2 0.95592 0.24375 3.74544 1.08234 3 0.46968 0.16029 2.02608 0.45066 4 1.00104 0.24882 3.92976 0.96684 5 1.15752 0.25935 3.38544 0.85785 6 0.67992 0.20943 2.61936 0.45381 7 1.27752 0.27417 4.28976 0.9471 8 1.43616 0.31941 3.37824 0.51933 9 0.95568 0.23907 2.23776 0.15225 10 0.87528 0.50115 4.02336 1.53468 11 1.02696 0.54093 3.86784 1.07541 12 0.54936 0.46683 2.36016 0.70812 13 1.07448 0.54951 4.24224 1.27134 14 0.7749 0.41298 2.32032 1.49226 15 0.9534 0.54587 3.32464 0.60677 16 1.6938 0.65946 5.6408 1.28307 17 1.8954 0.6714 4.53552 1.11692 18 1.2936 0.61173 2.87584 0.51158 19 1.1829 0.9282 5.31696 1.92831 20 1.3878 0.92201 4.66048 1.8716 21 0.8208 0.83494 3.47424 1.32326 22 1.4487 0.93218 5.76576 2.05229 23 1.65 0.98345 4.40352 1.43156 24 1.047 0.8924 3.28768 1.01774 25 1.7946 1.00069 5.324 1.67666 26 1.992 1.02014 5.12688 1.51706 27 1.3995 0.92113 2.99904 0.51927
- 52. ANOVA table for MRR (Brass tool) Source DF Seq SS MS F P Current 2 54.069 27.0346 15.51 0.004 On Time 2 66.855 33.427 19.18 0.002 Off Time 2 1.445 0.7224 0.41 0.078 Geometry 2 108.29 54.149 31.07 0.001 Current* on time 4 4.919 1.229 0.71 0.616 Current* off time 4 4.998 1.249 0.72 0.610 Current* Geometry 4 7.974 1.993 1.14 0.420 Error 6 10.458 1.743 Total 26 259.017
- 53. ANOVA table for TWR (Brass tool) Source DF Seq SS MS F P Current 2 642.48 321.24 553.17 0.000 On Time 2 7.024 3.512 6.05 0.036 Off Time 2 0.789 0.395 0.68 0.042 Geometry 2 15.92 7.96 13.71 0.006 Current* on time 4 6.109 1.527 2.63 0.139 Current* off time 4 1.894 0.474 0.82 0.559 Current * Geometry 4 4.183 1.046 1.80 0.247 Error 6 3.484 0.581 Total 26 681.885
- 54. ANOVA table for SR (Brass tool) Source DF Seq SS MS F P Current 2 34.422 17.220 15.60 0.004 On Time 2 97.858 48.928 44.35 0.00 Off Time 2 0.155 0.0773 0.07 0.033 Geometry 2 2.732 1.366 1.24 0.095 Current* on time 4 3.539 0.8848 0.80 0.566 Current* off time 4 16.732 4.1831 3.79 0.072 Current * Geometry 4 7.106 1.776 1.61 0.286 Error 6 6.620 1.103 Total 26 169.164
- 55. ANOVA table for EED (Brass tool) Source DF Seq SS MS F P Current 2 211.48 105.74 35.73 0.00 On Time 2 317.63 158.817 53.66 0.00 Off Time 2 19.93 9.96 3.37 0.105 Geometry 2 65.35 32.676 11.04 0.010 Current* on time 4 13.16 3.29 1.11 0.431 Current* off time 4 12.9 3.225 1.09 0.440 Current* Geometry 4 17.17 4.293 1.45 0.03 Error 6 17.76 2.96 Total 26 675.39
- 56. Residual Analysis in MRR (Brass tool) Residual analysis plot for MRR – to check model adequacy
- 57. Residual Analysis in TWR (Brass tool) Residual analysis plot for TWR – to check model adequacy
- 58. Residual Analysis in SR (Brass tool) Residual analysis plot for SR – to check model adequacy
- 59. Residual Analysis in EED (Brass tool) Residual analysis plot for EED – to check model adequacy
- 60. Findings - Factor effect on MRR (Brass tool) Main effect plot for MRR Interaction effect plot for MRR
- 61. Findings - Factor effect on TWR (Brass tool) Main effect plot for TWRInteraction effect plot for TWR
- 62. Findings - Factor effect on SR (Brass tool) Main effect plot for SRInteraction effect plot for SR
- 63. Findings - Factor effect on EED (Brass tool) Main effect plot for EEDInteraction effect plot for EED
- 64. Delta Values and Influence Ranking (Brass Tool) Current Pulse on time Pulse off time Tool cross sectional area or geometry On MRR Delta 3.4069 3.6513 0.5525 4.8549 Rank 3 2 4 1 On Delta 11.8630 1.1413 0.380 1.8234 TWR Rank 1 3 4 2 On SR Delta 2.711 4.614 0.161 0.694 Rank 2 1 4 3 On EED Delta 6.82432 7.930 2.038 3.646 Rank 2 1 4 3
- 65. Regression Equations (Brass Tool) In these equations W represents current in Amps, X represents pulse on time in µs, Y represents pulse off time in µs and Z represents tool cross sectional area in mm2 . MRR = 0.313 + 0.0210 W - 0.00245 X- 0.000063 Y+ 0.0131 Z R-Sq = 81.0% R-Sq(adj) = 77.5% TWR = - 0.764 + 0.0633 W - 0.000417 X+ 0.000014 Y+ 0.00195 Z R-Sq = 99.1% R-Sq(adj) = 99.0% SR = 2.84 + 0.109 W - 0.0122 X- 0.000980 Y+ 0.00125 Z R-Sq = 93.0% R-Sq(adj) = 91.7% EED = 1.01 + 0.0485 W- 0.00447 X- 0.00128 Y- 0.00928 Z R-Sq = 96.1% R-Sq(adj) = 95.4%
- 66. Model Validation (Brass Tool) Factors MRR TWR SR EED (mm3 /min) (mm3 /min) (µm) (º) 25A, 100µs, 100µs Predicted 1.4765 0.8991 4.5348 1.5669 circular Exp. Result 1.5251 0.9232 4.3223 1.605 Variation 3.19% 2.61% -4.92% 2.37% 25A, 100µs, 100µs Predicted 1.7036 0.9343 4.638 1.4504 square Exp. Result 1.824 0.9589 4.7772 1.4921 Variation 6.60% 2.57% 2.91% 2.79%
- 67. Statistical Multi Objective Optimization Flow Diagram
- 68. Optimum Parameters Found in EDM Optimum parameter code ( tool) Pulse Current (A) Pulse-on time (B) Pulse-off time(C) Geometry (D) MRR TWR SR EED A1 B1 C3 D2 ( Graphite) 15 50 200 cylindrical 0.39128 0.04103 1.40721 0.2145 Average Responses For all 27 experiments 0.87481 0.13716 2.4226 0.4357 Increase / reduction in response due to optimization - Graphite Tool -55% -70% -42% -51% A1 B3 C1 D2 (Copper) 15 200 50 cylindrical 0.42495 0.06981 1.4552 0.3242 Average Responses For all 27 experiments 0.6561 0.1783 1.937 0.6535 Increase / reduction in response due to optimization - Copper Tool -35% -61% -25% -50% A2 B3 C3 D2 (Brass) 20 200 200 cylindrical 1.03488 0.23976 2.35296 0.377 Average Responses For all 27 experiments 1.0609 0.5482 3.533 0.8674 Increase / reduction in response due to optimization - Brass Tool -2% -56% -33% -57%
- 69. Stages of Research in WEDM
- 70. Experiment - WEDM Taguchi Design • L18 orthogonal array • Six factors at three levels – Current – Pulse on time – Pulse off time – Wire Tension – Servo Voltage – Wire Speed Responses • MRR • Wire Offset • Surface Roughness
- 71. Process Parameters & Levels for WEDM (Decided through literature study and machine capability) PARAMETERS LEVELS L1 L2 L3 Pulse on-time (Ton) (µs) 3 8 13 Pulse off-time (Toff) (µs) 15 30 45 Current (I)(A) 8 10 12 Wire tension (WT) (N) 10 15 20 Servo voltage (SV) (V) 17 34 51 Wire speed (WS) (m/min) 5 10 15
- 72. Experiment Design (Taguchi design using Minitab with the Consideration for Interaction Effects – L18 OA) EXPERIMENT NUMBER PARAMETERS 1 2 3 4 5 6 1 1 1 1 1 1 1 2 1 2 2 2 2 2 3 1 3 3 3 3 3 4 2 1 1 2 2 3 5 2 2 2 3 3 1 6 2 3 3 1 1 2 7 3 1 2 1 3 2 8 3 2 3 2 1 3 9 3 3 1 3 2 1 10 1 1 3 3 2 2 11 1 2 1 1 3 3 12 1 3 2 2 1 1 13 2 1 2 3 1 3 14 2 2 3 1 2 1 15 2 3 1 2 3 2 16 3 1 3 2 3 1 17 3 2 1 3 1 2 18 3 3 2 1 2 3
- 73. Experimental Work Titanium Grade 5 sheets Work Piece Before Machining (SODICK AQ300LWEDM) WEDM Cutting Operation
- 74. Experimental Work Cut Pieces Titanium Grade 5 alloy Cut Pieces
- 75. Responses Measurement MRR: MRR =k.ρ.t.v t = thickness of the workpiece = 3 mm k = kerf width,mm ρ = density of the work piece = 4.42g/cm³ v = cutting speed, mm/min = Length of Cut / machining time WIRE OFFSET: Wire offset = (0.5 x diameter)+ overcut, mm Overcut =(kerf width – wire diameter)/2,mm Wire diameter= 0.25 mm Kerf Width = Total width – ( Remaining width – cut width), mm SR : Using surface roughness gauge. Schematic Diagram for Wire Offset
- 76. Kerf Width Calculation Exp. No. Machining time (mm) Total width of material, L(mm) Uncut width U1 (mm) Cut width, U(mm) Kerf width= L- U1-U, mm 1 10.47 89.88 84.6072 5.00 0.2728 2 5.28 84.6072 79.3186 5.00 0.2886 3 16.34 79.3186 74.0221 5.00 0.2965 4 4.55 74.0221 68.7344 5.00 0.2877 5 11.40 68.7322 63.4344 5.00 0.301 6 7.43 63.4344 58.1518 5.00 0.2816 7 6.39 58.1518 52.8476 5.00 0.3042 8 7.40 52.8476 47.5707 5.00 0.2769 9 6.54 47.5707 42.2707 5.00 0.3 10 7.24 42.52707 36.9747 5.00 0.2960 11 24.12 36.9747 31.6971 5.00 0.2776 12 9.07 31.6971 26.4128 5.00 0.2843 13 6.42 26.4128 21.1373 5.00 0.2755 14 5.05 21.1373 15.8499 5.00 0.2874 15 9.01 15.8499 10.5481 5.00 0.3018 16 6.07 10.5481 5.2514 5.00 0.2967 17 5.05 25.4107 20.1117 5.00 0.299 18 3.58 20.1117 148201 5.00 0.2919
- 77. WEDM Results Data Exp. No. Pulse On- time (µs) Pulse off-time (µs) Current (A) Wire Tension (N) Servo Voltage (V) Wire Speed (mm/min) MRR *10e-3 (g/min) Wire Offset (mm) Surface Roughness (µm) 1 3 15 8 10 17 5 8.6374 0.13640 1.699 2 3 30 10 15 34 10 18.119 0.14430 2.072 3 3 45 12 20 51 15 6.0100 0.14825 2.059 4 8 15 8 15 34 15 20.961 0.14385 2.229 5 8 30 10 20 51 5 8.7500 0.15050 1.957 6 8 45 12 10 17 10 12.660 0.14080 2.029 7 13 15 10 10 51 10 15.780 0.15210 2.527 8 13 30 12 15 17 15 12.400 0.13845 2.172 9 13 45 8 20 34 5 15.206 0.15000 2.346 10 3 15 12 20 34 10 13.550 0.14800 1.683 11 3 30 8 10 51 15 3.8100 0.13880 1.885 12 3 45 10 15 17 5 10.391 0.14215 1.687 13 8 15 10 20 17 15 14.225 0.13775 2.026 14 8 30 12 10 34 5 18.866 0.14370 2.065 15 8 45 8 15 51 10 11.103 0.15090 2.307 16 13 15 12 15 51 5 16.204 0.14385 2.062 17 13 30 8 20 17 10 19.627 0.14950 2.418 18 13 45 10 10 34 15 27.029 0.15950 2.355
- 78. ANOVA table for MRR Paramete rs DOF SS MS FCAL P TON 2 100.4951 50.24755 10.1945 0.023 TOFF 2 9.450045 4.725023 0.95864 0.09 I 2 14.02578 7.012891 1.42281 0.06 WT 2 8.734862 4.367431 0.88609 0.10 SV 2 113.9028 56.95141 11.5546 0.021 WS 2 15.70865 7.854325 1.59353 0.07 ERROR 5 24.64447 4.928894 TOTAL 17 286.9617
- 79. ANOVA table for Wire Offset Parameters DOF SS MS FCAL p TON 2 0.3872 0.1936 2.697 0.006 TOFF 2 0.3064 0.1532 2.1341 0.01 I 2 0.1604 0.0802 1.1168 0.023 WT 2 0.1277 0.0639 0.8896 0.030 SV 2 0.7067 0.3534 4.9222 0.001 WS 2 0.1501 0.0751 1.0457 0.0291 ERROR 5 0.3589 0.0718 TOTAL 17 2.1975
- 80. ANOVA table for SR Parameters DOF SS MS FCAL P TON 2 11.734 5.867 12.579 0.001 TOFF 2 0.5595 0.2797 0.5998 0.049 I 2 0.8522 0.4261 0.9135 0.032 WT 2 0.0071 0.0036 0.0077 0.09 SV 2 1.1806 0.5903 1.2657 0.01 WS 2 2.3673 1.1836 2.5378 0.009 ERROR 5 2.332 0.4664 TOTAL 17 19.033
- 81. Residual Analysis in MRR - WEDM (Brass wire) Residual analysis plot for MRR – to check model adequacy
- 82. Residual Analysis in Wire Offset - WEDM (Brass wire) Residual analysis plot for EED – to check model adequacy
- 83. Residual Analysis in SR - WEDM (Brass wire) Residual analysis plot for SR – to check model adequacy
- 84. Findings - Factor effect on MRR - WEDM (Means Graphs) Factors effect plot for MRR ( Means) Interaction effect plot for MRR ( Means)
- 85. Findings - Factor effect on Wire Offset - WEDM (Means Graphs) Factors effect plot for Wire Offset ( Means) Interaction effect plot for Wire Offset ( Means)
- 86. Findings - Factor effect on SR - WEDM (Means Graphs) Factors effect plot for SR( Means) Interaction effect plot for SR ( Means)
- 87. Delta Values and Influence Ranking - WEDM (Brass Tool) TON TOFF Current WT SV WS On MRR Delta 5.68 1.69 2.14 1.55 6.15 2.23 Rank 2 5 4 6 1 3 On Delta 0.35 0.29 0.22 0.20 0.44 0.20 Wire Offset Rank 2 3 4 5 1 6 On SR Delta 1.969 0.418 0.525 0.047 0.563 0.835 Rank 1 5 4 6 3 2
- 88. Regression Model for MRR Where A is pulse on time in micro seconds, B is pulse off time in micro seconds, C is current in Amperes, D is wire tension in Newton, E is servo voltage in Volts and F is wire speed in mm/min. MRR = - 83.2 + 2.54 A - 0.0354 A2 + 0.193 B + 0.00161 B2 + 10.4 C - 0.526 C2 - 0.76 D + 0.0226 D2 + 2.21 E - 0.0340 E2 + 1.74 F - 0.0782 F2 - 0.0381A*B S. no Actual values Simulated Values Error % 1 16.2040 15.52715 4.18 2 19.6270 19.0175 3.11 3 27.0293 25.99515 3.83 Percentage of Error during Validation for 16, 17 & 18 Experiments
- 89. Regression Model for Wire Offset Where A is pulse on time in micro seconds, B is pulse off time in micro seconds, C is current in Amperes, D is wire tension in Newton, E is servo voltage in Volts and F is wire speed in mm/min. WIRE OFFSET = 0.0032 + 0.00053 A + 0.000024 A2 - 0.000050 B + 0.000005 B2 + 0.0200 C - 0.000999 C2 - 0.00108 D + 0.0000048 D2 + 0.00145 E - 0.000018 E2 + 0.00329 F - 0.000157 F2 - 0.000006 A*B Percentage of Error during Validation S.no Actual values Simulated values Error % 1 0.14385 0.143752 0.07 2 0.14950 0.145118 2.93 3 0.15950 0.155128 2.74
- 90. Regression Model for SR Where A is pulse on time in micro seconds, B is pulse off time in micro seconds, C is current in Amperes, D is wire tension in Newton, E is servo voltage in Volts and F is wire speed in mm/min. SURFACE ROUGHNESS = - 0.843 + 0.188 A - 0.00264 A2 + 0.0448 B - 0.000245 B2 + 0.022 C - 0.00310 C2 - 0.0767 D + 0.00281 D2 + 0.0664 E - 0.000932 E2 + 0.161 F - 0.00687 F2 - 0.00301 A*B Percentage of Error during Validation S.no Actual values Simulated values Error % 1 2.062 2.0796 -0.85 2 2.418 2.454492 -1.51 3 2.355 2.3873 -1.37
- 91. Contour Plot based Parameter Optimization in MRR (Maximization Objective)
- 92. Contour Plot based Parameter Optimization in MRR (Maximization Objective)
- 93. Contour Plot based Parameter Optimization in MRR (Maximization Objective)
- 94. Contour Plot based Parameter Optimization in MRR (Maximization Objective)
- 95. Contour Plot Findings for Maximum MRR Sl.No WEDM Parameters Range for maximum MRR 1 Pulse on time 11 to 13 µs 2 Pulse off time 25 to 35 µs 3 Current 9 to 11 A 4 Wire tension 10 to 14 N 5 Servo voltage 25 to 40 V 6 Wire speed 11 to 15 mm/min
- 96. Contour Plot based Parameter Optimization in Wire Offset (Minimization Objective)
- 97. Contour Plot based Parameter Optimization in Wire Offset (Minimization Objective)
- 98. Contour Plot based Parameter Optimization in Wire Offset (Minimization Objective)
- 99. Contour Plot based Parameter Optimization in Wire Offset (Minimization Objective)
- 100. Contour Plot Findings for Minimum Wire Offset Sl.No WEDM Parameters Range for Minimum Wire Offset 1 Pulse on time 3 to 6 µs 2 Pulse off time 15 to 35 µs 3 Current 9 to 12 A 4 Wire tension 10 to 14 N 5 Servo voltage 15 to 22 V 6 Wire speed 12.5 to 15 mm/min
- 101. Contour Plot based Parameter Optimization in SR (Minimization Objective)
- 102. Contour Plot based Parameter Optimization in SR (Minimization Objective)
- 103. Contour Plot based Parameter Optimization in SR (Minimization Objective)
- 104. Contour Plot based Parameter Optimization in SR (Minimization Objective)
- 105. Contour Plot Findings for Minimum Surface Roughness Sl.No WEDM Parameters Range for Minimum SR 1 Pulse on time 3 to 7 µs 2 Pulse off time 15 to 20 µs 3 Current 11 to 12 A 4 Wire tension 14 to 20 N 5 Servo voltage 15 to 30 V 6 Wire speed 5 to 7 mm/min
- 106. WEDM Optimization Results Optimization Technique Pulse On- time Pulse off- time Current Wire Tension Servo Voltage Wire Speed MRR (g/min) Wire Offset Surface Roughnes s (µs) (µs) (A) (N) (V) (mm/min) *10e-3 (mm) (µm) SA 13 30 12 15 34 15 23.2994 0.138347 2.453248 Average Values for All 18 Experiments 14.0738 0.145489 2.087667 Performance Improvement/Reduction 65.55% -4.90% 17.51% BFOA 11.2172 32.2172 11.2172 14.2172 35.2172 12.2172 24.44258 0.167533 2.493216 Average Values for All 18 Experiments 14.0738 0.145489 2.087667 Performance Improvement/Reduction 73.67% 15.15% 19.43% Cuckoo 12.0259 34.0259 11.0259 14.0259 36.0259 12.0259 24.68218 0.168673 2.512946 Average Values for All 18 Experiments 14.0738 0.145489 2.087667
- 107. Conclusions – EDM - Slide1 By comparison brass tool is the best from MRR point of view. Its average MRR is 1.170 mm3 /min whereas graphite is at 0.8748 mm3 /min and copper is at 0.6561mm3 /min which is the least performing in terms of MRR. From TWR point of view, graphite is the best and brass is the worst. TWR average values are for graphite 0.1371 mm3 /min, for copper 0.1783 mm3 /min and for brass 0.5769 mm3 /min. From SR point of view, copper is the best with 1.937µm followed by graphite at 2.4226µm and then brass at 3.8257µm. From EED point of view, graphite is the best as it is not wearing because of very high melting point , gives 0.4351º . Copper is at 0.6535º and brass results in 1.0938º.
- 108. Optimum values of factors were found out for each studied tool material Regression models found out were validated and found to be very good in prediction and are very useful for the industries who work with titanium grade 5 alloy for prediction and to improve productivity with in the studied range of parameters. In general, the interactive effects among the electrical parameters were not influencing the responses significantly. Based on the delta values, the current and pulse on time are the two most important and critical process parameters that affects all the studied responses. Among these two factors pulse on time is more critical. Conclusions – EDM – Slide2
- 109. Except MRR, all other responses were negatively affected by increasing pulse current value. Pulse off time is not affecting any response except surface roughness in the studied range. Tool cross sectional area is influencing the entry exit deviation rather than the geometry. Higher cross sectional area reduces the deviation. Tool cross sectional area is not influencing in any way the surface roughness. Conclusions – EDM – Slide3
- 110. To obtain maximum MRR, the percentage of contribution of each parameters were in the order Servo Voltage(SV), Pulse On Time(TON), Wire Speed(WS), Current(I), Pulse Off Time(TOFF), Wire Tension(WT). Among them SV and TON are the most influential parameters and other parameters are less significant as their percentage of contribution for maximizing the MRR is less than 10%. To obtain minimum Wire offset value, the percentage of contribution of each parameters were in the order SV, TON, TOFF, I, WT, WS. Among them SV, TON and TOFF are the most influential parameters and other parameters are less significant as their percentage of contribution for minimizing the Wire offset value is less than 10%. Conclusions – WEDM - Slide1
- 111. To obtain minimum Surface Roughness value, the percentage of contribution of each parameters were in the order TON, WS, SV, I, TOFF, WT. Among them TON and WS are the most influential parameters and other parameters are less significant as their percentage of contribution for minimizing the Surface Roughness value is less than 10%. WEDM regression models found out were validated and found to be very good in prediction and are very useful for the industries who work with titanium grade 5 alloy for prediction and to improve productivity. Conclusions – WEDM – Slide2
- 112. For brass wire, optimum parameters found through simulated annealing based multi objective optimization were 12A pulse current, 13µs pulse on time, 30 µs pulse off time, wire tension 15N, servo voltage 34V and wire speed 15 mm/min which resulted in 65.5% increase in MRR and 4.9% reduction in wire offset compared to the averages of corresponding responses in all 18 experiments. However SR is increased by 17.5% which is a compromise made for getting high MRR. Conclusions – WEDM – Slide3
- 113. Optimum parameters found through BFOA based multi objective optimization were 11.2172 A pulse current, 11.2172 µs pulse on time, 32.2172 µs pulse off time, wire tension 14.2172N, servo voltage 35.2172V and wire speed 12.2172 mm/min which resulted in 73.67% increase in MRR , 15.15% increase in wire offset and 19.43% increase in SR compared to the averages of corresponding responses in all 18 experiments which is a compromise made for getting very high MRR. Conclusions – WEDM – Slide4
- 114. Optimum parameters found through COA based multi objective optimization were 11.0259 A pulse current, 12.0259 µs pulse on time, 34.0259 µs pulse off time, wire tension 14.0259 N, servo voltage 36.0259 V and wire speed 12.0259 mm/min which resulted in 75.38 % increase in MRR , 15.93 % increase in wire offset and 20.37 % increase in SR compared to the averages of corresponding responses in all 18 experiments which is a compromise made for getting very high MRR. Conclusions – WEDM – Slide5
- 115. Suggestions for Future Work 1. Efforts should be made to investigate the effects of WEDM process parameters on performance measures in a cryogenic cutting environment as this material is suitable to be used in cryogenic environment. 2. The effect of process parameters such as flushing pressure, conductivity of dielectric, wire diameter, work piece height etc. may also be investigated. 3. In this research for multi objective optimization, weighing method was used to combine many objectives into one objective function. Instead multi-layer simultaneous search based algorithms can be found to be used in such environments.
- 116. Publications – International Journals 1. Saravanan P Sivam, Antony L Michael Raj, Satish Kumar S, Varahamoorthy R and Dinakaran D. “Effects of electrical parameters, its interaction and tool geometry in electric discharge machining of titanium grade 5 alloy with graphite tool”. Proc. IMechE Part B: J. Engineering Manufacture, January 2013 vol. 227 no. 1 119-131. 2. Saravanan P Sivam, Antony L MichaelRaj, Satish Kumar S, Prbhakaran G, Dinakaran D and Ilamkumaran V2014. “Statistical multi objective optimization of machining parameters in EDM of titanium grade 5 alloy with graphite electrode”, Proceedings of Institution of Mechanical Engineers: Part B – Journal of Engineering Manufacture, Vol. 228, Issue 7 July 2014 pp. 736 – 743. DOI: 10.1177/0954405413511073. 3. Saravanan P Sivam, Antony L MichaelRaj and Satish Kumar S, 2013.” Influence Ranking of Process Parameters in Electric Discharge Machining of Titanium Grade 5 Alloy Using Brass Electrode”, International Journal of Mechanical Engineering and Technology, 4(5):71-80.
- 117. Publications – International Conferences 1. Influence ranking of process parameters in electric discharge machining of titanium grade 5 alloy using graphite tool electrode. NCT 3rd International Conference Proceedings, Oman 2012 , p17-18. 2. Influence Ranking of Process Parameters in Electric Discharge Machining of Titanium Grade 5 Alloy Using Copper Electrode, Proceedings of ICRDPET, 2013, IEEE explore transactions, India 2013, p72-78 ISBN 978 -1-4673-4948-2 2013IEEE.
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- 119. References – Slide 2 Armendia, M., Garay, A., Iriarte, L. M. and Arrazola, P.J. 2010, Comparison of the machinability of Ti6Al4V and TIMETAL®54M using uncoated WC-Co tools. J. Mater. Process. Technol., 210, 197-203. Belgassim, O. and Abusaada, A. 2012, “Investigation of the influence of EDM parameters on the overcut for AISI D3 tool steel”, Proc. IMechE Part B: J. Engineering Manufacture, February 226, 365-370 Belgassim, O. and Abusaada, A. 2012, “Investigation of the influence of EDM parameters on the machined surface hardness of AISI D3 tool steel”, Journal of Engineering Research, March 16, 31-38 Ming, Q.Y. and He, L.Y., 1995, “Powder-suspension dielectric fluid for EDM”, Journal of materials processing technology, 52, 44–54. Wong, Y.S., Lim, L.C. and Lee, L.C., 1995, “Effect of flushing on electro-discharge machined surfaces”, Journal of Materials Processing Technology, 48, 299–305. Kunieda, M. and Yoshida, M., 1997, “Electrical discharge machining in gas”, CIRP Annals –Manufacturing Technology”, 46(1), 143-146. Wong, Y.S., Lim, L.C., Rahuman, I. and Tee, W.M., 1998, “Near-mirror-finish phenomenon in EDM using powder-mixed dielectric”, Int. J. Adv. Manuf. Technol., 79, 30–40. Chen, S.L., Yan, B.H. and Huang, F.Y., 1999, “Influence of kerosene and distilled water as dielectric on the electric discharge machining characteristics of Ti-6Al-4V”, Journal of Materials Processing Technology, 87, 107–111. Xu, M.G., Zhang, J.H., Yule, Zhang, Q.H. and Ren, S.F., 2009 “Material removal mechanisms of cemented carbides machined by ultrasonic vibration assisted EDM in gas medium”, Journal of materials processing technology, 209, 1742– 1746. Zhang, Q.H., Zhang, J.H., Deng, J.X., Qin, Y. and Niu, Z.W., 2002, “Ultrasonic vibration electrical discharge machining in gas”, Journal of Materials Processing Technology, 129,135–138.
- 120. References – Slide 3 Kunieda, M., Miyoshi, Y., Takaya, T., Nakajima, N., Bo, Y.Z. and Yoshida, M., 2003, “High speed 3D milling by dry EDM”, CIRP Annals—Manufacturing Technology, 52, 147–150. Yu, Z.B., Jun, T. and Masanori K., 2004, “Dry electrical discharge machining of cemented carbide”, Journal of Materials Processing Technology, 149, 353–357. Zhang, Q.H., Du, R., Zhang, J.H. and Zhang, Q., 2006, “An investigation of ultrasonic-assisted electrical discharge machining in gas”, Int. J. Mach. Tools Manuf., 46, 1582–1588. Wang, C.C. and Yan, B.H., 2000, “Blind-hole drilling of Al2O3/6061Al composite using rotary electro-discharge machining”, Journal of Materials Processing Technology, 102, 90–102. Kunieda, M. and Muto, H., 2000, “Development of multi-spark EDM”, Ann. CIRP, 49 (1), 119–122. Bayramoglu, M. and Duffill, A.W., 1995, “Manufacturing linear and circular contours using CNC EDM and frame type tools”, Int. J. Mach. Tools Manuf., 35(8), 1125–1136. Bayramoglu, M. and Duffill, A.W., 2004, “CNC EDM of linear and circular contours using plate tools”, Journal of Materials Processing Technology, 148, 196–203. Chen, S.L., Lin, M.H., Hsieh, S.F. and Chiou. S.Y., 2008, “The characteristics of cutting pipe mechanism with multi-electrodes in EDM”, Journal of materials processing technology,203, 461– 464. Han, F., Wang, Y. and Zhou, M., 2009 “High-speed EDM milling with moving electric arcs”, Int. J. Mach. Tools Manuf., 49, 20–24. Aspinwall, D.K., Dewes, R.C., Burrows, J.M. and Paul, M.A., 2001 Hybrid high speed machining (HSM): system design and experimental results for grinding/HSM and EDM/HSM. Ann. CIRP, 50 (1), 145–148.
- 121. References – Slide 4 Tzeng, Y.F. and Lee, C.Y., 2001, “Effects of powder characteristics on electro discharge machining efficiency”, Int. J. Adv. Manuf. Technol., 17, 586–592. Zhao, W.S., Meng, Q.G. and Wang, Z. L. 2002, “The application of research on powder mixed EDM in rough machining”, Journal of materials processing technology, 129, 30–33. Ghoreishi, M. and Atkinson, J., 2002, “A comparative experimental study of machining characteristics in vibratory, rotary and vibro- rotary electro-discharge machining”, Journal of Materials Processing Technology, 120, 374–384. Mohan B., Rajadurai A. and Satyanarayana K.G., 2004, “Electric discharge machining of Al–SiC metal matrix composites using rotary tube electrode”. Journal of Materials Processing Technology, 153–154, 978–985. Singh, S., Kansal, H.K. and Kumar, P., 2005, “Parametric optimization of powder mixed electrical discharge machining by response surface methodology”, Journal of Materials Processing Technology, 169 (3), 427– 436. Saravanan P Sivam, Antony L Michael Raj, Satish Kumar S, Varahamoorthy R and Dinakaran D. 2013, “Effects of electrical parameters, its interaction and tool geometry in electric discharge machining of titanium grade 5 alloy with graphite tool”,. Proc. IMechE Part B: J. Engineering Manufacture; 227 (1): 119-131. Paulraj Sathiya, Aravindan S. and Noorul Haq A. 2006, “Optimization for friction welding parameters with multiple performance characteristics”, Int. J. Mech Mater; 3: 309-318. Jeyapaul R., Shahabudeen P. and Krishnaiah K. 2006, “Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm”, Int. J Adv. Manuf. Technol.; 30: 870-878. Satishkumar S. and Asokan P. 2008, “Selection of optimal conditions for CNC Multi tool Drilling System using nontraditional techniques”, International Journal of Machining and Machinability of Materials; 3: 190-207. Satishkumar S., Asokan P. and Kumanan S. 2006, “Optimization of Depth of Cut in Multi-Pass Turning using nontraditional optimization techniques”, International Journal of Advanced Manufacturing Technology; 29: 230-238.
- 122. References – Slide 5 Huang J.T. and Liao Y.S. 2003, “Optimization of machining parameters of wire-EDM based on grey relational and statistical analyses”, Int J Prod Res; 41(8): 1707-1720. Amini H., SoleymaniYazdi M.R. and Dehghan G.H. 2011, “Optimization of process parameters in wire electrical discharge machining of TiB2nano composite ceramic”, ProcIMechE Part B: J Engineering Manufacture; 225(12): 2220-2227. Kondayya D. and Gopalakrishna A. 2011, “An integrated evolutionary approach for modeling and optimization of wire electrical discharge machining. Proc I Mech E Part B: J Engineering Manufacture; 225 (4): 549-567. Lin Y.C., Chow H.M., Wang D.A., Chen Y.F. and Lin Y.J. 2012, “Optimization of EDM Parameters on Nonconductive Ceramics Using Grey Relational Analysis”, Proc. of ISEM-XVI. Li C.J., Guo Y.F., Bai J.C., Zhu G.Z., Lu Z.E. and Feng Y. 2011, “ Parameter Optimization for Surface Roughness of Multiple Cut on HS-WEDM Based on Genetic Algorithm”, Proc. of ISEM-XVI. Dauw D.F., Sthioul H., Delpretti R. and Tricarico C. 1989, “Wire analysis and control for precision EDM cutting”, Ann CIRP 38(1):191–194 Puri A.B. and Bhattacharyya B. 2003, “Modeling and analysis of the wire tool vibration in wire-cut EDM”, Int J Mater Process Technol., 141(3): 295–301 Probirsaha, Abhijitsingha, Surjya K. Pal and Parthasaha, 2008, “Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite”, Int J Adv Manuf Technol., 39:74–84 Lee S.H. and Li X.P. 2001, “Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide”, J Mater Process Technol., 115:344–358 Saha P., Singha A., Pal S.K. and Saha P. 2007, “A neural network approach for modeling the wire electro-discharge machining of tungsten carbide cobalt composite”, Proc Global Conference on Production and Industrial Engineering (CPIE-2007), India.
- 123. References – Slide 6 Nilesh G. Patil and Brahmankar P. K. 2010, “Some studies into wire electro-discharge machining of alumina particulate-reinforced aluminum matrix composites”, Int J Adv Manuf Technol 48:537–555. Gatto A. Iuliano 1997, “Cutting mechanisms and surface features of WED machined metal matrix composites”, J Mater Process Technology, 65: 209–214 Harshit K. Dave, Keyur P. Desai and Harit K. Raval 2008, “Investigations on Prediction of MRR and Surface Roughness on Electro Discharge Machine Using Regression Analysis and Artificial Neural Network Programming”, WCECS 2008, October 22 - 24, San Francisco, USA Sarkar S., Mitra S. and Bhattacharyya B. 2006,” Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model”, Int J Adv Manuf Technol., 27: 501–508. Liao Y.S., Huang J.T. and Su H.C. 1997, “A study on the machining – parameters optimisation of wire electrical discharge machining”, J Mater Process Technol., 71:487–493 Puri A.B. and Bhattacharyya B. 2003, “An analysis and optimization of thegeometrical inaccuracy due to wire lag phenomenon in WEDM”, Int J Mach Tools Manuf., 43(2):151–159. Scott F. Miller, Albert J. Shih and Jun Qub 2004, “Investigation of the spark cycle on material removal rate in wire electrical discharge machining of advanced materials”, International Journal of Machine Tools & Manufacture, 44, 391–400. Liao Y.S., Huangb, J.T. and Chena Y.H. 2004, “A study to achieve a fine surface finish in Wire-EDM”, Journal of Materials Processing Technology 149, 165–171. Hideo Takinoa, Toshimitsu Ichinohe and Katsunori Tanimoto 2005, “High-quality cutting of polished single-crystal silicon by wire electrical discharge machining”, Precision Engineering, 29, 423–430. Dinesh Rakwal and Eberhard Bamberg 2009, “Slicing, cleaning and kerf analysis of germanium wafers machined by wire electrical discharge machining”, Journal of materials processing technology, 209, 3740–3751.
- 124. References – Slide 7 Probir Saha , Abhijit Singha , Surjya K. Pal and Partha Saha 2008, “Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite”, Int J Adv Manuf Technol., 39, 74– 84. Nilesh G. Patil and Brahmankar P. K. 2010, “Some studies into wire electro discharge machining of alumina particulate-reinforced aluminum matrix Composites”, Int J Adv Manuf Technol., 48:537–555. Aminollah Mohammadi, Alireza Fadaei Tehrani, Ehsan Emanian and Davoud Karim 2008, “A new approach to surface roughness and roundness improvement in wire electrical discharge turning based on statistical analyses”, Int J Adv Manuf Technol., 39:64–73. Sarkar S., Mitra S. and Bhattacharyya B. 2006, “Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model”, Int J Adv Manuf Technol., 27: 501–508. Kodalagara Puttanarasaiah Somashekhar, Nottath Ramachandran and Jose Mathew 2010, “Material removal characteristics of microslot (kerf) geometryin μ-WEDM on aluminium”, Int J Adv Manuf Technol, 51: 611–626. Mahapatra S.S. and Amar Patnaik 2007, “Optimization of wire electrical discharge machining(WEDM) process parameters using Taguchi method”, Int J Adv Manuf Technol., 34:911–925. Kapil Kumar and Sanjay Agarwal 2007, “Multi-objective parametric optimization on machining with wire electric discharge machining”, Int J Adv Manuf Technol., DOI 10.1007/s00170-011-3833-1. Probir Saha and Debashis Tarafdar 2009, “Modelling of WEDM of TiC/Fein situ metal matrix composite using normalized RBFN with enhanced k-means clustering technique”, Int J Adv Manuf Technol., 43:107–116. Rong Tai Yang &Chorng JyhTzeng& Yung Kuang Yang &Ming Hua Hsieh 2007, “Optimization of wire electrical discharge machining process parameters for cutting tungsten”, Int J Adv Manuf Technol., DOI 10.1007/s00170-011-3576-z Ramakrishnan R. and Karunamoorthy L. 2006, “Multi response optimization of wire EDM operationsusing robust design of experiments”, Int J Adv Manuf Technol., 29: 105–112.
- 125. References – Slide 8 Ramezan Ali and Mahdavi Nejad 2011, “Modeling and Optimization of Electrical Discharge Machining of SiC Parameters, Using Neural Network and Non-dominating Sorting Genetic Algorithm (NSGAII)”, Materials Sciences and Applications, 2, 669-675. Dase R.K. and Pawar D.D. 2007, “Application of Artificial Neural Network for stock market predictions: A review of literature”, Int J Adv Manuf Technol., 34:911–925 Susanta Kumar Gauri and Shankar Chakraborty 2010, “A study on the performance of some multi-response Optimization methods for WEDM processes”, Int. J Adv. Manuf. Technol., 49:155–166. Ramezan Ali Mahdavi Nejad 2011, “Modeling and Optimization of Electrical Discharge Machining of SiC Parameters, Using Neural Network and Non-dominating Sorting Genetic Algorithm (NSGA II)”, Materials Sciences and Applications, 2, 669-675. Tong LI, Su CT and Wang CH. 1977, “The optimization of multi response problems in the Taguchi method”, Int J Qual. Reliab. Manage., 14(4): 367-380. Chen M.C. and Tsai D.M. 1996, “A simulated annealing approach for optimization of multi-pass turning operations”, International Journal of Production Research, 34:10, 2803-2825. Xiaohui Yan, Yunlong Zhu, Hao Zhang, Henning Chen and Ben Niu 2012, “An adaptive bacterial foraging optimization algorithm with life cycle and social learning”, Discrete Dynamics in Nature and Society; Volume 2012, Article ID 40978, DOI : 10.1155/2012/409478. Kevin M. Passino 2010, “Bacterial foraging optimization”, Int. Journal of Swam Intelligence Research, 1(1), 1-16. Passino K.M. 2002, “Bio mimicry of bacterial foraging for distributed optimization and control”, IEEE Control Systems Magazine, 22(3), 52-67. Dong Hwa Kim, Ajith Abraham and Jae Hoon Cho 2007, “A hybrid genetic algorithm and bacterial foraging approach for global optimization”, Information Sciences, 177: 3918–3937.
- 126. References – Slide 9 Samaneh Zareh, Hamid Haj Seyedjavadi and Hossein Erfani 2012, Grid Scheduling using Cooperative BFO Algorithm, American Journal of Scientific Research ISSN 1450-223X Issue 62, pp.78-87. Tang W. J. and Wu Q.H. 2006, “Bacterial Foraging Algorithm For Dynamic Environments”, IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006. Dasgupta S., Das S., Abraham A. and Biswas A. 2009, “Adaptive computational chemotaxis in bacterial foraging optimization: An analysis,” IEEE Transactions on Evolutionary Computation, 13:4, 919–941 Rajabioun R. 2011, “Cuckoo Optimization Algorithm,” Applied Soft Computing, 11:8, 5508-5518. Humar Katramanli 2012,, A modified cuckoo optimization algorithm for engineering optimization. Int. journal of Future Computer and Communication, 1:2, 199-201. Yang X.S. and Deb S. 2010, "Engineering optimization by cuckoo search," International Journal of Mathematical Modelling and Numerical Optimization, 4, pp. 330–343. Hongqing Zheng and Yongquan Zhou 2012,, “A Novel Cuckoo Search Optimization Algorithm Base on Gauss Distribution", Journal of Computational Information Systems, 8:10,4193–4200 Kaveh A., Bakhshpoori T. and Ashoory M 2012, “an efficient optimization procedure based on cuckoo search algorithm for practical design of steel structures”, international journal of optimization in civil engineering Int. J. Optim. Civil Eng., 2(1):1-14 Yang X.S. and Deb S. (2010), "Engineering Optimisation by Cuckoo Search", Int. J. Mathematical Modelling and Numerical Optimisation, 1:4, 330–343. Saeed Balochian and Eshagh Ebrahimi 2013, "Parameter optimization via cuckoo optimization algorithm of fuzzy controller for liquid level control ", Journal of Engineering, Vol. 2013. Article ID 982354. DOI -10.1155/2013/982354.
- 127. References – Slide 10 Yang X.S. and Deb S. 2011, “Multiobjective cuckoo search for design optimization,” Computers and Operations Research. Walton, S. Hassan, O., Morgan, K. and Brown M. R 2011, “Modified cuckoo search: a new gradient free optimization algorithm Chaos,” Solutions & Fractals, 44: 710–718. Aghaei A. and Azadi S. 2013, “Optimizing Azadi Controller with COA,” International Journal of Computer Applications, 61: 8. Elman C Jameson (2001), “Electrical Discharge Machining”, Society of Manufacturing Engineers, Michigan, USA. Carl Sommer, Steve Sommer and Carol Sommer (2000), “Wire EDM Hand Book”, Advance Publishing Inc, Houston, USA. Chris Wood(2010), “Boeing 747 Owner’s Workshop Manual”, Zenith Press, USA. Ranjit Roy (1990), “A Primer on the Taguchi Method”, Society of Manufacturing Engineers, Michigan, USA. Ross, P.J. (1988), “Taguchi techniques for quality engineering”, McGraw-Hill Book Company, New York. Ross, P.J. (1996), “Taguchi techniques for Quality Engineering”, McGraw-Hill Book Company, New York. Roy, R.K. (2001), “Design of Experiments Using the Taguchi approach” Canada John Wiley & Sons. Barker, T.B. (1986), “Quality engineering by design: Taguchi s Philosophy”, Quality Progress, December, 33-42.‟ Barker, T.B. (1990), “Engineering quality by design”, Marcel Dekker, Inc., New York. C. H. Che-Haron, A. Jawaid, “The Effect of Machining on Surface Integrity of Titanium Alloy Ti–6% Al-4% V.,” Journal of Materials Processing Technology, Vol. 166, No. , 2005, pp. 188-192. K. Dass and S. Chauhan, "Machinability Study of Titanium (Grade-5) Alloy Using Design of Experiment Technique," Engineering, Vol. 3 No. 6, 2011, pp. 609-621.
- 128. THANK YOU One & All

- Aero Space and Defense industries Association of Europe – ( ASD )
- 38 grades in titanium alloys. Grade 5 is having very wide applications than others called as industrial work horse. solid solution and precipitation strengthening -It has a density of roughly 4420 kg/m3, Young&apos;s modulus of 110 GPa, and tensile strength of 1000 MPa.[8] By comparison, annealed type 316 stainless steel has a density of 8000 kg/m3, modulus of 193 GPa, and tensile strength of only 570 MPa.[9] And tempered 6061 aluminium alloy has 2700 kg/m3, 69 GPa, and 310 MPa, respectively.[ First four grades are pure titanium . Al is alpha stabiliser. Vanadium is beta stabiliser. Presence of these two makes it alph beta alloy which can be heat treatable. Which is an another advantage.
- 38 grades in titanium alloys. Grade 5 is having very wide applications than others called as industrial work horse. solid solution and precipitation strengthening -It has a density of roughly 4420 kg/m3, Young&apos;s modulus of 110 GPa, and tensile strength of 1000 MPa.[8] By comparison, annealed type 316 stainless steel has a density of 8000 kg/m3, modulus of 193 GPa, and tensile strength of only 570 MPa.[9] And tempered 6061 aluminium alloy has 2700 kg/m3, 69 GPa, and 310 MPa, respectively.[ First four grades are pure titanium . Al is alpha stabiliser. Vanadium is beta stabiliser. Presence of these two makes it alph beta alloy which can be heat treatable. Which is an another advantage.
- also referred to as spark machining, spark eroding, burning, die sinking series of rapidly recurring current discharges between two electrodes, separated by a dielectric liquid and subject to an electric voltage the intensity of the electric field in the volume between the electrodes becomes greater than the strength of the dielectric (at least in some point(s)), which breaks, allowing current to flow between the two electrodes. Advantages and disadvantages of EDM over conventional machining – Time consuming, material of any hardness can be machined just it needs to be electrically conductive, No mechanical forces to cause mechanical stresses and in turn microscopic cracks, costly process for unique materials used in costly applications. Useful for making dies.
- The final dimensions of the work piece is 100 x 50 x 3 mm. Its density is 4.42 g cm-3. Graphite Density (g/cm3) 1.3-1.95
- The work piece top and bottom faces were ground to a surface finish using a surface grinding machine before conducting the experiments. The initial weights of the work piece and the tool were weighed using an accurate weighing machine. The work piece was held on the machine table using a specially designed fixture. The work piece and tool were connected to positive and negative terminals of power supply, respectively. At the end of each experiment, the workpiece and tool were removed, washed, dried and weighed on an electronic balance. The machining time was determined using stop-watch. Existence of adaptive control in maintaining the gap Specifications of Power Supply PARAMETER UNITS V5030 Maximum Current A 25 Maximum Open Circuit Voltage V 75-80
- The work piece top and bottom faces were ground to a surface finish using a surface grinding machine before conducting the experiments. The initial weights of the work piece and the tool were weighed using an accurate weighing machine. The work piece was held on the machine table using a specially designed fixture. The work piece and tool were connected to positive and negative terminals of power supply, respectively. At the end of each experiment, the workpiece and tool were removed, washed, dried and weighed on an electronic balance. The machining time was determined using stop-watch. Existence of adaptive control in maintaining the gap Specifications of Power Supply PARAMETER UNITS V5030 Maximum Current A 25 Maximum Open Circuit Voltage V 75-80
- The number of degree of freedom of a sum of squares is equal to the number of independent elements in that sum of square. Variance = Sum of squares / degree of freedom Mean Square ( MS) = Sum of Squares / Degree of Freedom P value represents level of significance ( probability of mistake) which should be lower than 5% so that confidence level will be at 95%. F distribution = MS – treatments / MS – error With this F value, p value can be found from probability density diagrams. Examination of residuals should be an automatic part of any ANOVA. If the model is adequate residuals should be structure less that is they should not form any patterns which can be checked by graph which is called graphical analysis of residuals for model adequacy. Source is source of variation.
- Eij =Yij
- If pulse on time is less, there will be more number of sparks in a particular time in turn more erosion or machining of metal. During pulse on time the dielectric strength of the liquid is broken down by the intensity of electric field and a high energy electrical spark is on set. That raises the temperature locally to over 8000 degree Celsius for very short period of time. During pulse off time the dielectric fluid washes away the removed metal from work piece as well as tool.
- If pulse on time is less, there will be more number of sparks in a particular time in turn more erosion or machining of metal. During pulse on time the dielectric strength of the liquid is broken down by the intensity of electric field and a high energy electrical spark is on set. That raises the temperature locally to over 8000 degree Celsius for very short period of time. During pulse off time the dielectric fluid washes away the removed metal from work piece as well as tool.
- If pulse on time is less, there will be more number of sparks in a particular time in turn more erosion or machining of metal. During pulse on time the dielectric strength of the liquid is broken down by the intensity of electric field and a high energy electrical spark is on set. That raises the temperature locally to over 8000 degree Celsius for very short period of time. During pulse off time the dielectric fluid washes away the removed metal from work piece as well as tool.
- The number of degree of freedom of a sum of squares is equal to the number of independent elements in that sum of square. Variance = Sum of squares / degree of freedom Mean Square ( MS) = Sum of Squares / Degree of Freedom P value represents level of significance ( probability of mistake) which should be lower than 5% so that confidence level will be at 95%. F distribution = MS – treatments / MS – error With this F value, p value can be found from probability density diagrams. Examination of residuals should be an automatic part of any ANOVA. If the model is adequate residuals should be structure less that is they should not form any patterns which can be checked by graph which is called graphical analysis of residuals for model adequacy. Source is source of variation.
- Eij =Yij
- The number of degree of freedom of a sum of squares is equal to the number of independent elements in that sum of square. Variance = Sum of squares / degree of freedom Mean Square ( MS) = Sum of Squares / Degree of Freedom P value represents level of significance ( probability of mistake) which should be lower than 5% so that confidence level will be at 95%. F distribution = MS – treatments / MS – error With this F value, p value can be found from probability density diagrams. Examination of residuals should be an automatic part of any ANOVA. If the model is adequate residuals should be structure less that is they should not form any patterns which can be checked by graph which is called graphical analysis of residuals for model adequacy. Source is source of variation.
- Eij =Yij
- Eij =Yij