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Multi response analysis of ul-752 and bs-476 glass chemical assisted ultrasonic machining based on GRA Method

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https://www.eduzhai.net International Journal of Materials Engineering 2017, 7(2): 33-43 DOI: 10.5923/j.ijme.20170702.03 Multi-response Analysis of Chemical Assisted Ultrasonic Machining of UL-752 and BS-476 Glass by GRA approach Kanwal Jeet Singh1,*, Inderpreet Singh Ahuja2, Jatinder Kapoor3 1Department of Mechanical Engineering, (GZSCCET), Bathinda, Punjab, India 2Department of Mechanical Engineering, (UCoE-PU) Patiala, Punjab, India 3Department of Production Engineering, (GNDEC) Ludhiana, Punjab, India Abstract This paper is developed an innovative process of chemical assisted ultrasonic machining of polycarbonate bullet proof UL-752 and acrylic heat resistant BG-476 glass and conducted an investigational to optimize the machining parameters associated with multiple performance characteristics using Grey relational analysis. Machining of polycarbonate bullet proof UL-752 and acrylic heat resistant BS-476 glass are difficult process via conventional machining, however, it can be easily machined by Ultrasonic machining. Carefully selected parameters gives the optimum results. In this experimental work input parameters abrasive slurry concentration, type of abrasive, power rate, grit size of abrasive particles, hydro-fluoride acid concentration and tool material are selected. The effect of input parameters viz material removal rate, tool wear rate and surface roughness are investigate. Grey relational analysis and analysis of variance are performed to optimize the input parameters and better output results. In PBPG UL-752, increment in material removal rate by 80%, tool wear rate by 50% and surface roughness by 40%. In other hand, in AHRG BS-476, increment in material removal rate by 70%, tool wear rate by 30% and surface roughness by 25%. Keywords USM, Polycarbonate bullet proof glass, Acrylic Heat resistant glass, HF acid, Grey Relational Analysis 1. Introduction Ultrasonic machining (USM) is known as the non-conventional machining process (Kuriakose et al. 2017; Wang et al. 2016). In which the material is removed by erosion mechanism. The selection of input process parameters play an important role in the USM process (Li et al. 2016; Lin et al. 2016). In this paper, the input parameters are abrasive slurry concentration, type of abrasive particles, power rate, grit size of abrasive particles, hydro-fluoride (HF acid) concentration, tool material are selected (Khairay 1990; Choi et al. 2007). The output parameters are material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Machining of polycarbonate bullet proof (PBPG UL-572) and acrylic heat resistant BS-476 (AHRG BS-476) are too tough job, because it have alternative layers of glass, polycarbonate and acrylic material. Acrylic and polycarbonate material are easily machined by conventional processes and glass is machined by non-conventional processes like USM, water jet machining (WJM) and abrasive water jet machining (AWJM) (Choi et al. 2007; * Corresponding author: khalsa.kanwal@yahoo.com (Kanwal Jeet Singh) Published online at https://www.eduzhai.net Copyright © 2017 Scientific & Academic Publishing. All Rights Reserved Guzza et al. 2004). Other non-conventional processes like Laser beam machining (LBM) is not utilized because it produced heat effected zone, electron beam machining (EBM) is applicable only on conductive materials and conventional machining will damage the PBPG UL-752 and AHRG BS-476. In last USM is best alternative for machining of this material. Some important properties of PBPG UL-752 and AHRG BS-476 are shown in the Table 1. Table 1. Important properties of PBPG UL-752 and AHRG BS-476 Properties Tensile strength (Depend on thickness) Compressive Strength Linear expansion (20 to 300°C) Thermal Conductivity at 23°C Reactivity with HF Acid Hardness Density 7 g/cm3 PBPG UL-752 120-180 MPa 1000 MPa (at 73oF) 9x10-6 m/(m-k) 0.30 W/(m-K) poor 58 HRC 7 gm/cm3 AHRG BS-476 105- 155 MPa 1200 MPa (at 73oF) 8.23 x10-6 m/(m-k) 0.86 W/(m-K) poor 61 HRC 8.3 g/cm3 In the experiment, selected parameters having three different level shown in Table 2. Design of experiment is prepared by Minitab 6.7 software in which L27 orthogonal array is used. The levels are selected by pilot experiments. For calculating MRR and TWR the initial and final weight of 34 Kanwal Jeet Singh et al.: Multi-response Analysis of Chemical Assisted Ultrasonic Machining of UL-752 and BS-476 Glass by GRA approach tools and work sample respectively measured by weight machine and surface roughness of check by Taylor Hobson Surtronic 25 surface roughness tester. spindle, 25.4 mm diameter cylindrical horn, and power supply (Hofy 2012; Jatinder & Khamba 2010). The power supply unit convert 50Hz electric supply input to sonic frequency 20 kHz output. 500W Sonic-Mill vibrating kit having piezoelectric transducer, it convert electric signal input into mechanical vibration output signal (Lee & Chan 1997; Kanwal Singh & Ahuja 2014; Kanwal Singh & Singla 2014; Singh and Khamba 2008). The amplitude of the vibration is 0.0237-0.0268 mm and frequency of vibration 20 kHz ± 250 Hz. Static load for feeding the USM tool is fixed at 1.757 kg and abrasive slurry flow rate is 30 L/min (Singh & Khamba 2007; Thoe et al. 1998; Vinod & Aniruddha 2008; Zhang et al. 1999). Table 2. Different input or controllable machining parameters & their levels Factor Level 1 Levels Level 2 Figure 1. Schematic Diagram of Chemical Assisted Ultrasonic Machine Experiment are performed on Sonic Mill 500W USM and schematic diagram of machine is shown in Fig.1. 500W Sonic-Mill ultrasonic machine have vibrating spindle kit, feeding system at constant pressure and abrasive slurry flow pump system. Fig. 1 show the schematic figure of USM apparatus. The ultrasonic vibration kit contains an ultrasonic Concentration (A) Abrasive (B) Power Rate (C) Grit Size (D) HF Acid (E) Tool Material (F) 20 Al2O3+B2C 20 280 0.5% D2 30 SiC+ B2C 40 400 1% High-Carbon Steel Table 3. Design of experimentation (Orthogonal Array L27) and their levels Level 3 40 Al2O3+ SiC+ B2C 60 600 1.5% High-Speed Tool Steel Trail 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. Concentration 20 20 20 20 20 20 20 20 20 30 30 30 30 30 30 30 30 30 40 40 40 40 40 40 40 40 40 Type of Abrasive Al2O3+B2C Al2O3+B2C Al2O3+B2C SiC+B2C SiC+B2C SiC+B2C Al2O3+SiC+B2C Al2O3+SiC+B2C Al2O3+SiC+B2C Al2O3+B2C Al2O3+B2C Al2O3+B2C SiC+B2C SiC+B2C SiC+B2C Al2O3+SiC+B2C Al2O3+SiC+B2C Al2O3+SiC+B2C Al2O3+B2C Al2O3+B2C Al2O3+B2C SiC+B2C SiC+B2C SiC+B2C Al2O3+SiC+B2C Al2O3+SiC+B2C Al2O3+SiC+B2C Power Rate 20 20 20 40 40 40 60 60 60 40 40 40 60 60 60 20 20 20 60 60 60 20 20 20 40 40 40 Grit Size 280 280 280 400 400 400 600 600 600 600 600 600 280 280 280 400 400 400 400 400 400 600 600 600 280 280 280 HF Acid 0.5% 1% 1.5% 0.5% 1% 1.5% 0.5% 1% 1.5% 0.5% 1% 1.5% 0.5% 1% 1.5% 0.5% 1% 1.5% 0.5% 1% 1.5% 0.5% 1% 1.5% 0.5% 1% 1.5% Tool Material D2 HCS HSTS D2 HCS HSTS D2 HCS HSTS HCS HSTS D2 HCS HSTS D2 HCS HSTS D2 HSTS D2 HCS HSTS D2 HCS HSTS D2 HCS International Journal of Materials Engineering 2017, 7(2): 33-43 35 Trail 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. Table 4. Design of experimentation (Orthogonal Array L27) and their levels Polycarbonate Bullet proof (UL-752) glass MRR (????????????????????????/????????????????????????) TWR (????????????????????????/????????????????????????) (SR) Ra (Micron) 6.47 0.089209 1.57 6.42 0.079284 1.86 6.83 0.038295 1.42 7.53 0.082232 1.51 7.77 0.074125 1.68 6.88 0.036479 1.59 8.19 0.082788 1.33 7.48 0.084926 1.43 7.07 0.029241 1.29 5.45 0.048754 1.24 6.29 0.033464 1.34 5.67 0.072275 1.18 6.33 0.079673 1.46 6.95 0.053041 1.77 6.12 0.106128 1.32 7.52 0.085062 1.44 7.64 0.054312 1.59 6.81 0.095251 1.48 6.26 0.046235 1.12 5.63 0.085143 1.27 6.38 0.084632 1.09 7.12 0.040692 1.14 6.26 0.086954 0.97 6.43 0.069166 1.02 7.23 0.060492 0.99 8.75 0.239698 1.06 8.27 0.111256 1.10 Acrylic Heat Resistant (BS-476) Glass MRR (????????????????????????/????????????????????????) TWR (????????????????????????/????????????????????????) (SR) Ra (Micron) 4.25 0.088853 1.29 3.86 0.071071 1.86 4.32 0.037189 1.42 4.78 0.084845 1.51 4.25 0.064513 1.68 4.35 0.037273 1.59 4.84 0.080889 1.33 5.77 0.106384 1.43 5.48 0.033212 1.29 4.38 0.057549 1.03 4.34 0.035739 1.34 4.60 0.089643 1.18 5.03 0.098258 1.46 5.17 0.059348 1.77 4.63 0.107305 1.32 5.43 0.099815 1.44 5.00 0.049939 1.59 4.74 0.103339 1.48 3.75 0.042181 0.93 4.27 0.102664 1.27 3.89 0.083415 1.09 4.29 0.036937 1.14 4.67 0.109564 0.97 4.16 0.071609 1.02 5.02 0.063506 0.99 5.09 0.211508 1.06 4.59 0.101542 1.1 2. Material and Methods Selected parameters and levels are shown in Table 2. For the design of experiment orthogonal array L27 is used and design is prepared by Minitab 6.7 software. The design of experiment is shown in Table 3. All the experiments are performed according to the design experiment. MRR and TWR are calculated by the equation 1 and equation 2, in which density of work material ρ is 8.3 gm/cm3, Wi is initial weight, Wf final weight after processing, t is time take in machining. Ti initial and Tf weight of tool and ρ = Density of D2 Steel 7.83 gm/cm3, Density of HC steel 7.85 gm/cm3, Density of HST steel 8.13 gm/cm3 (Hasani et al. 2012; Hasiao et al. 2008) ???????????????????????? = ???????????????? −???????????????? ???????? ???????? ???????? 1000 (????????????????3/????????????????????????) (1) ???????????????????????? = ????????????????−???????????????? ???????? ???????? ???????? 1000 (????????????????3/????????????????????????) (2) Surface roughness is measured is Ra, it is the universally recognised and most used international parameter of roughness. It is the arithmetic mean of the absolute departure of the roughness profile from the mean line. After machining the MRR and TWR are calculated and SR is checked, machining data is shown in Table 4. In which MMR and TWR is calculated in mm3/min and surface roughness in Ra. 3. Results and Discussion In grey relation analysis, data pre-processing is necessary to sequence scatter range. Data pre-processing is a process in which original sequence is transferred into comparable sequence. The experiment results are normalized in the range between zero (0) and one (1). Depending on output parameters, data pre-processing methodologies are adopted (Lin et al. 2002; Lin & Lee 2009; You et al. 2017). MRR is the governing output parameter in USM, which decided the machinability of work material under deliberation. “Larger-the-better” characteristics is used for 36 Kanwal Jeet Singh et al.: Multi-response Analysis of Chemical Assisted Ultrasonic Machining of UL-752 and BS-476 Glass by GRA approach MRR to normalize the original sequence by equation 3. X * i (k ) = Xi (K ) − MinXi (K ) MaxXi (K ) − MinXi (K ) (3) Where, X * i ( K ) is the sequence after the data processing, Xi (K ) is the comparability sequence, K=1 and k=4 for MRR; i= 1,2,3………27 for experiment number 1 to 27. TWR and SR are the important measure of USM, these output parameters are represent the machining accuracy under selected input parameters (Patil & Patil 2016; Das et al. 2016). To get the optimum performance the “Smaller-the-better” characteristic has been preferred to normalize the original sequence date by equation 4. X * i ( K ) = MaxXi (K ) − Xi (K ) MaxXi (K ) − MinXi (K ) (4) Where, X * i ( K ) is the sequence after the data processing, Xi (K ) is the comparability sequence, K=2, K=5 for TWR and K=3, K=6 for SR; i= 1,2,3………27 for experiment number 1 to 27. X * i ( K ) is the value after grey relational generation, Min Xi (K ) and Max Xi (K ) are the smallest and largest value of Xi (K ) . After normalized MRR, TWR and SR of PBPG UL-752 and AHRG BS-476 comparable sequence is shown in the Table 5. Now ∆0i (K ) is the deviation sequence between reference sequence X 0 i (K ) and the comparability sequence X * i ( K ) (Ahmad et al. 2016). Deviation sequence is calculate by the equation 5 and maximum and minimum difference is found, K=1, 2 and 3 and i= 1, 2, 3…27. ∆0i (K )= X0 (K ) − Xi (K ) (5) Trail Reference Sequence 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. Table 5. The sequences of each performance characteristic after data processing Polycarbonate Bullet proof (UL-752) glass MRR TWR SR 1 1 1 0.309091 0.715058 0.325843 0.293939 0.762217 0 0.418182 0.956979 0.494382 0.630303 0.74821 0.393258 0.70303 0.786731 0.202247 0.433333 0.965608 0.303371 0.830303 0.745568 0.595506 0.615152 0.735409 0.483146 0.490909 1 0.640449 0 0.907283 0.696629 0.254545 0.979934 0.58427 0.066667 0.795521 0.764045 0.266667 0.760369 0.449438 0.454545 0.886913 0.101124 0.20303 0.634666 0.606742 0.627273 0.734763 0.47191 0.663636 0.880874 0.303371 0.412121 0.686349 0.426966 0.245455 0.919252 0.831461 0.054545 0.734378 0.662921 0.281818 0.736806 0.865169 0.506061 0.94559 0.808989 0.245455 0.725773 1 0.29697 0.810294 0.94382 0.539394 0.851509 0.977528 1 0 0.898876 0.854545 0.6103 0.853933 Acrylic Heat Resistant (BS-476) Glass MRR TWR SR 1 1 1 0.247525 0.687929 0.612903 0.054455 0.787662 0 0.282178 0.977694 0.473118 0.509901 0.710409 0.376344 0.247525 0.824444 0.193548 0.29703 0.977223 0.290323 0.539604 0.732596 0.569892 1 0.589604 0.462366 0.856436 1 0.612903 0.311881 0.863502 0.892473 0.292079 0.985827 0.55914 0.420792 0.683498 0.731183 0.633663 0.63518 0.430108 0.70297 0.853412 0.096774 0.435644 0.584438 0.580645 0.831683 0.626447 0.451613 0.618812 0.906184 0.290323 0.490099 0.606682 0.408602 0 0.949696 1 0.257426 0.610468 0.634409 0.069307 0.718429 0.827957 0.267327 0.979108 0.774194 0.455446 0.571768 0.956989 0.20297 0.784645 0.903226 0.628713 0.830092 0.935484 0.663366 0 0.860215 0.415842 0.616761 0.817204 International Journal of Materials Engineering 2017, 7(2): 33-43 37 The deviation sequence table is shown in the Table 6, Maximum ( ∆Max ) and Minimum ( ∆Min ) are obtained and shown below. ∆Max = ∆10 (1) = ∆26 (2) = ∆02 (3) = ∆19 (4) = ∆26 (5) = ∆02 (6) =1 ∆Min = ∆26 (1) = ∆09 (2) = ∆23 (3) = ∆08 (4) = ∆09 (5) = ∆19 (6) =0 After per-processing data, the next step in calculate the Grey relational coefficient and Grey relation grade with the pre-processed data (Lin et al. 2009). It define the relationship between ideal and actual normalized results. Grey relational coefficient ξ can be expressed as equation 6 is shown below. ξi (K) = ∆Min + ρ∆Max ∆0i (K ) + ρ∆Max (6) Where, ∆0i (K ) is the deviation sequence of the reference sequence X 0 i (K ) and the comparability sequence, ρ is distinguishing or identification coefficient. In this calculation ρ =0.5 because all parameters are given equal preference (Lin 2012). The Grey relation coefficient for each experiment of the L27 orthogonal array is calculated by using equation 6 and shown in Table 7. After obtaining the Grey relation coefficient, the Grey relation grade γ i is obtained by averaging the Grey relation coefficient corresponding to each performance characteristic and represent by ξi (1) , ξi (2) , ξi (3) ξi (4) , ξi (5) and ξi (6) Equation 7 (Manivanna et al. 2011) show the general formula of Grey relation grade and equation 8 is for three output parameters, shown in Table 7. ∑ γ i= 1 n n k =1{ξi ( K )} (7) γ =i 1 3 {ξi (1) + ξi (2) + ξi (3)} (8) Deviation Sequence 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. ∆0i (1) 0.690909 0.706061 0.581818 0.369697 0.29697 0.566667 0.169697 0.384848 0.509091 1 0.745455 0.933333 0.733333 0.545455 0.79697 0.372727 0.336364 0.587879 0.754545 0.945455 0.718182 0.493939 0.754545 0.70303 0.460606 0 0.145455 Table 6. The deviation sequences ∆0i (2) 0.284942 0.237783 0.043021 0.25179 0.213269 0.034392 0.254432 0.264591 0 0.092717 0.020066 0.204479 0.239631 0.113087 0.365334 0.265237 0.119126 0.313651 0.080748 0.265622 0.263194 0.05441 0.274227 0.189706 0.148491 1 0.3897 ∆0i (3) 0.674157 1 0.505618 0.606742 0.797753 0.696629 0.404494 0.516854 0.359551 0.303371 0.41573 0.235955 0.550562 0.898876 0.393258 0.52809 0.696629 0.573034 0.168539 0.337079 0.134831 0.191011 0 0.05618 0.022472 0.101124 0.146067 ∆0i (4) 0.752475 0.945545 0.717822 0.490099 0.752475 0.70297 0.460396 0 0.143564 0.688119 0.707921 0.579208 0.366337 0.29703 0.564356 0.168317 0.381188 0.509901 1 0.742574 0.930693 0.732673 0.544554 0.79703 0.371287 0.336634 0.584158 ∆0i (5) 0.312071 0.212338 0.022306 0.289591 0.175556 0.022777 0.267404 0.410396 0 0.136498 0.014173 0.316502 0.36482 0.146588 0.415562 0.373553 0.093816 0.393318 0.050304 0.389532 0.281571 0.020892 0.428232 0.215355 0.169908 1 0.383239 ∆0i (6) 0.387097 1 0.526882 0.623656 0.806452 0.709677 0.430108 0.537634 0.387097 0.107527 0.44086 0.268817 0.569892 0.903226 0.419355 0.548387 0.709677 0.591398 0 0.365591 0.172043 0.225806 0.043011 0.096774 0.064516 0.139785 0.182796 38 Kanwal Jeet Singh et al.: Multi-response Analysis of Chemical Assisted Ultrasonic Machining of UL-752 and BS-476 Glass by GRA approach Expt. No. {ξi (1)} 1. 0.419847 2. 0.414573 3. 0.462185 4. 0.574913 5. 0.627376 6. 0.46875 7. 0.746606 8. 0.565069 9. 0.495495 10. 0.333333 11. 0.40146 12. 0.348837 13. 0.405406 14. 0.478261 15. 0.385514 16. 0.572917 17. 0.597826 18. 0.45961 19. 0.398551 20. 0.345912 21. 0.410448 22. 0.503049 23. 0.398551 24. 0.415617 25. 0.520505 26. 1 27. 0.774647 Table 7. The calculated Grey Relational Grade and its order in the optimization process Grey Relational Coefficient {ξi (2)} {ξi (3)} {ξi (4)} {ξi (5)} {ξi (6)} Grey Relation Grade γ m= 1 6 {ξi (1) + ξi (2) + ξi (3) +ξi (4) + ξi (5) + ξi (6)} 0.63699 0.677706 0.920775 0.665079 0.700998 0.935643 0.66275 0.653944 1 0.843573 0.961416 0.709744 0.676013 0.815545 0.577812 0.653392 0.80759 0.614514 0.860959 0.653064 0.655141 0.90186 0.645805 0.724947 0.771021 0.333333 0.561987 0.425837 0.333333 0.497207 0.451776 0.385281 0.41784 0.552795 0.491713 0.581699 0.622377 0.546012 0.679389 0.475936 0.35743 0.559749 0.486339 0.41784 0.465968 0.7479 0.597315 0.787611 0.723577 1 0.89899 0.956989 0.831775 0.773914 0.39921 0.34589 0.410569 0.505 0.39921 0.415638 0.520619 1 0.776924 0.420833 0.413934 0.463303 0.577143 0.627329 0.469768 0.748148 0.567416 0.495098 0.333333 0.402391 0.349481 0.405623 0.478673 0.385496 0.573864 0.597633 0.461187 0.61571 0.701914 0.957293 0.633239 0.740131 0.956431 0.651547 0.549212 1 0.785548 0.972435 0.612368 0.578155 0.77329 0.546113 0.572375 0.842012 0.559711 0.908589 0.562093 0.639737 0.959892 0.538658 0.698954 0.746371 0.333333 0.566098 0.563636 0.333333 0.486911 0.444976 0.382716 0.413333 0.537572 0.481865 0.563636 0.823009 0.531429 0.65035 0.467337 0.356322 0.54386 0.476923 0.413333 0.458128 1 0.57764 0.744 0.688889 0.920792 0.837838 0.885714 0.781513 0.732283 0.510205 0.467792 0.62249 0.545831 0.539285 0.601273 0.611982 0.623634 0.736292 0.638112 0.637781 0.577332 0.529998 0.56803 0.513803 0.585016 0.60767 0.508838 0.708222 0.523069 0.597736 0.697148 0.663747 0.660307 0.742411 0.646265 0.645019 Rank 24 26 12 19 20 14 13 11 2 9 10 17 21 18 23 16 14 25 3 22 15 4 5 6 1 7 8 The higher value of Grey relation grade is represent that the corresponding experiment result is much closer to the ideally normalized value. Experiment number 25 get the best multiple performance characteristics among the 27 experiment because it have the highest value of grey relation grade. Now the experimental design is orthogonal, it is possible to separate out the effect of each parameters on the basis of Grey relation grade. Mean of Grey relation grade is calculated for level 1, 2 and 3 by averaging the Grey relation grade of the experiment 1to 9, 10 to18 and 19 to 27 are shown in Table 8. The mean of Grey relation grade for abrasive, power rate, grit size, HF acid and tool material are calculated in same manner. The total mean of Grey relation grade for 27 experiment is also shown in the Table 8. *Level for optimum grey relational grade. Optimum level parameters are find out from response table and shown in the Fig.2. Larger value of Grey relation grade is closer to the ideal value. Therefore, the optimum parameters setting for higher MRR and lower TWR and SR are A3B3C2D3E1F3. Furthermore, analysis of variance (ANOVA) is performed on Grey relation grade to achieve contribution of each input parameter affecting the output parameters. ANOVA for Grey relational grade is shown in Table 9. In addition, F-test is also used to find out the percentage contribution of each parameters. From Table 9 it is clear that material of tool have the significant role in the machining which have 30% contribution, 25% contribution of concentration, 21% contribution of grit size, 9% contribution of abrasive, 4% contribution of HF acid and 3% contribution of power rate in the machining of PBPG UL-752 and AHRG BS-476. International Journal of Materials Engineering 2017, 7(2): 33-43 39 Grey Relational Grade Graph (Grey Relational Grade Vs Level) Concent rat ion 0.66 Abrasive Power Rate Grit Size HF Acid Tool Material Grey Relational Grade 0.64 0.62 0.60 0.58 0.56 20 30 4A0l2O3+B2C SiCA+l2BO23C+ SiC+B2C 20 40 60 280 400 600 0.5% 1% 1.5% D2 HCS HSTS Figure 2. Effect of USM parameters on the multiple performance characteristics Symbol A B C D E F Table 8. Response Table for the Grey Relational Grade Machining Parameters Grey Relation Grade Level 1 Level 2 Level 3 Main Effect (Max- Min) Concentration 0.5843 0.5741 0.6538 0.0797 Abrasive 0.5870 0.5910 0.6341 0.0471 Power Rate 0.5915 0.6193 0.6014 0.0278 Grit Size 0.5829 0.5797 0.6496 0.0699 HF Acid 0.6188 0.5864 0.6070 0.0324 Tool Material 0.5668 0.5874 0.6579 0.0911 Total men value of the Grey relational Grade γ m = 0.6040 Rank 2 4 6 3 5 1 Parameter Concentration (A) Abrasive (B) Power Rate (C) Grit Size (D) HF Acid (E) Tool Material (F) Error Total Table 9. ANOVA of Grey relation grade Degree of Freedom 2 2 2 2 2 2 6 18 Sum of Squares 0.033848 0.012288 0.003568 0.028051 0.004844 0.041104 0.002746 0.136067 Mean Squares 0.016924 0.006144 0.001784 0.014025 0.002422 0.020552 0.000458 F Ration 36.98 13.42 3.90 30.64 5.29 44.90 2.41 Percentage Contribution 24.87% 9.03% 2.62% 20.61% 3.56% 30.20% 2.01% After getting the optimum parameters for machining the experiment is performed by those input setting (A3B3C2D3E1F3). Fig.3 show the Scanning electron microscope (SEM) images of PBPG UL-752 machining setting A1B1C1D1E1F1, In which machining by USM is performed and some crack are also found on the work surface. In other hand in Fig. 4 the USM machining of PBPG UL-752 is performed by optimum parameters which are found by Grey relational analysis A3B3C2D3E1F3, there is smoother and crack free surface. Similarly, in Fig.5 show the 40 Kanwal Jeet Singh et al.: Multi-response Analysis of Chemical Assisted Ultrasonic Machining of UL-752 and BS-476 Glass by GRA approach Scanning electron microscope (SEM) images of AHRG BS-476 machining setting A1B1C1D1E1F1, in which machining by USM is performed and some crack are also found on the work surface. In other hand in Fig. 6 the USM machining AHRG BS-476 is performed by optimum parameters which are found by Grey relational analysis A3B3C2D3E1F3, there is smoother and crack free surface. MRR and TWR are also compared between optimum Grey relational analysis A3B3C2D3E1F3 and A1B1C1D1E1F1 input parameters. It observed that optimum parameters (A3B3C3D3E1F3) gives 73.02% improved MRR with comparison of A1B1C1D1E1F1 USM experiment setting. TWR is decreased by 37.25%. It is evident from SEM image, optimum parameters setting also give the better surface roughness which is 43.33% improved. Fig 7 shown the percentage contribution of optimum Grey relational analysis parameters. Figure 3. SEM image of PBPG UL-752 A1B1C1D1E1F1 experiment Figure 4. SEM image of PBPG UL-752 A3B3C2D3E1F3 optimum Grey relational analysis Figure 5. SEM image of AHRG BS-476A1B1C1D1E1F1 experiment International Journal of Materials Engineering 2017, 7(2): 33-43 41 Figure 6. SEM image of AHRG BS-476A3B3C2D3E1F3 optimum Grey relational analysis Figure 7. Percentage contribution of factor on Grey Relational Grade Table 10. Improvement in Grey relational grade with optimized USM machining parameters Optimal Machining Parameters Condition Description Machining Parameters in First trail of OA Grey Theory Prediction Design PBPG UL-752 Grey Theory Prediction Design AHRG BS-476 Level A1B1C1D1E1F1 A3B3C2D3E1F3 A3B3C2D3E1F3 MRR (mm3/min) 5.36 9.41 8.64 TWR (mm3/min) 0.089031 0.04866 0.06102 SR (micron) 1.43 0.94 1.08 Grey Relational Grade 0.5642 0.6751 Improvement in Grey relational grade =0.1109 Confirmation test is carried out to verify the improvement of performance characteristics in machining of PBPG UL-752 and AHRG BS-476 by USM. The optimum parameters are shown in the Table 10. The estimated Grey relational grade γˆ using the optimal level of machining parameters can be calculated by using equation 9 (Meena & Azad 2012; Singh et al. 2004; Sreenivasulu & srinivasarao 2012). ∑ γˆ = γ m + in=1{γ i − γ m} (9) Where, γ m is the total mean of Grey relational grade, γ i is mean of the Grey relational grade at optimum level and n is the number of parameters that significantly affect multiple-performance characteristics. It is clearly show that the multiple-performance characteristics in USM process is greatly improved through this study. 4. Conclusions The optimum machining parameters are identify by Grey relational grade for multiple performance characteristics that is MRR, TWR and SR. This experimental research paper presented the multi-objective optimization of USM machining parameters of polycarbonate bullet proof UL-752 and acrylic heat resistant BS-476 glass for drilling application by Grey relational analysis method. Following conclusion are conclude from the experimentation analysis. 1. Concentration of abrasive slurry, concentration and grit size of abrasive play the significant role for optimum output performance parameters. 42 Kanwal Jeet Singh et al.: Multi-response Analysis of Chemical Assisted Ultrasonic Machining of UL-752 and BS-476 Glass by GRA approach 2. ANOVA of Grey relational grade for multiple performance characteristics reveals that the concentration have the significant role in the MRR. 3. Based on SEM images, it is evident that optimum parameter improve the surface roughness and give better smooth surface. 4. PBPG UL-752 have improvement in MRR, TWR and SR is 80%, 50% and 40% respectively, based on confirmation test. 5. AHRG BS-476 have improvement in MRR, TWR and SR is 70%, 30% and 25% respectively, based on confirmation test. 6. 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