ULTRASONIC MACHINING OF POLYCARBONATE BULLET PROOF & ACRYLIC HEAT RESISTANT GLASS AND OPTIMIZATION BY GREY RELATIONAL ANALYSIS

Transcription

1 ULTRASONIC MACHINING OF POLYCARBONATE BULLET PROOF & ACRYLIC HEAT RESISTANT GLASS AND OPTIMIZATION BY GREY RELATIONAL ANALYSIS Kanwal Jeet Sngh a*, Inderpreet Sngh Ahuja b, Jatnder Kapoor c a Department of Mechancal Engneerng, (GZSCCET), Bathnda, Punjab, Inda b Department of Mechancal Engneerng, (UCoE-PU) Patala, Punjab, Inda c Department of Producton Engneerng, (GNDEC) Ludhana, Punjab, Inda A B S T R A C T Ths paper s developed an nnovatve process of chemcal asssted ultrasonc machnng of polycarbonate bullet proof UL-752 and acrylc heat resstant BG-476 glass and conducted an nvestgatonal to optmze the machnng parameters assocated wth multple performance characterstcs usng Grey relatonal analyss. Machnng of polycarbonate bullet proof UL-752 and acrylc heat resstant BS-476 glass are dffcult process va conventonal machnng, however, t can be easly machned by Ultrasonc machnng. Carefully selected parameters gves the optmum results. In ths expermental work nput parameters abrasve slurry concentraton, type of abrasve, power rate, grt sze of abrasve partcles, hydro-fluorde acd concentraton and tool materal are selected. The effect of nput parameters vz materal removal rate, tool wear rate and surface roughness are nvestgate. Grey relatonal analyss and analyss of varance are performed to optmze the nput parameters and better output results. In PBPG UL-752, ncrement n materal removal rate by 75.58%, tool wear rate by 45.34% and surface roughness by 34.18%. In other hand, n AHRG BS-476, ncrement n materal removal rate by 61.24%, tool wear rate by 31.46% and surface roughness by 23.85% K E Y W O R D S USM; Polycarbonate Bullet Proof Glass; Acrylc Heat Resstant Glass; HF Acd; Grey Relatonal Analyss. A R T I C L E I N F O Receved 11 Jan 2017 Accepted 22 June 2017 Avalable onlne 30 June 2017 * Correspondng author: Kanwal Jeet Sngh E-mal: khalsa.kanwal@yahoo.com,tel.:

2 Introducton Ultrasonc machnng (USM) s known as the non-conventonal machnng process (Kurakose et al. 2017; Wang et al. 2016) In whch the materal s removed by eroson mechansm. The selecton of nput process parameters play an mportant role n the USM process (L et al. 2016; Ln et al. 2016). In ths paper, the nput parameters are abrasve slurry concentraton, type of abrasve partcles, power rate, grt sze of abrasve partcles, hydro-fluorde (HF acd) concentraton, tool materal are selected (Kharay 1990; Cho et al. 2007). The output parameters are materal removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Machnng of polycarbonate bullet proof (PBPG UL-572) and acrylc heat resstant BS-476 (AHRG BS- 476) are too tough job, because t have alternatve layers of glass, polycarbonate and acrylc materal. Acrylc and polycarbonate materal are easly machned by conventonal processes and glass s machned by non-conventonal processes lke USM, water jet machnng (WJM) and abrasve water jet machnng (AWJM) (Cho et al. 2007; Guzza et al. 2004). Other non-conventonal processes lke Laser beam machnng (LBM) s not utlzed because t produced heat effected zone, electron beam machnng (EBM) s applcable only on conductve materals and conventonal machnng wll damage the PBPG UL-752 and AHRG BS-476. In last USM s best alternatve for machnng of ths materal. Some mportant propertes of PBPG UL-752 and AHRG BS-476 are shown n the Table 1. Table 1. Important propertes of PBPG UL-752 and AHRG BS-476 Propertes PBPG UL-752 AHRG BS-476 Tensle strength (Depend on thckness) MPa MPa Compressve Strength 1000 MPa ( at 73 o F) 1200 MPa (at 73 o F) Lnear expanson (20 to 300 o C) 9x10-6 m/(m-k) 8.23 x10-6 m/(m-k) Thermal Conductvty at 23 o C 0.30 W/(m-K) 0.86 W/(m-K) Reactvty wth HF Acd poor poor Hardness 58 HRC 61 HRC Densty 7 g/cm 3 7 gm/cm g/cm 3 In the experment, selected parameters havng three dfferent level shown n Table 2. Desgn of experment s prepared by Mntab 6.7 software n whch L 27 orthogonal array s used. The levels are selected by plot experments. For calculatng MRR and TWR the ntal and fnal weght of tools and work sample respectvely measured by weght machne and surface roughness of check by Taylor Hobson Surtronc 25 surface roughness tester. Experment are performed on Sonc Mll 500W USM and schematc dagram of machne s shown n Fg W Sonc-Mll ultrasonc machne have vbratng spndle kt, feedng system at constant pressure and abrasve slurry flow pump system. Fg. 1 show the schematc fgure of USM apparatus. The ultrasonc vbraton kt contans an ultrasonc spndle, 25.4 mm dameter cylndrcal horn, and power supply (Hofy 2012; Jatnder & Khamba 2010). The power supply unt convert 50Hz electrc supply nput to sonc frequency 20 khz output. 500W Sonc-Mll vbratng kt havng pezoelectrc transducer, t convert electrc sgnal nput nto mechancal vbraton output sgnal (Lee & Chan 1997; Kanwal Sngh & Ahuja 2014; Kanwal Sngh & Sngla 2014; Sngh and Khamba 2008). The ampltude of the vbraton s mm and frequency of vbraton 20 khz Hz. Statc load for feedng the USM tool s fxed at kg and abrasve slurry flow rate s 30 L/mn ( Sngh & Khamba 2007; Thoe et al. 1998; Vnod & Anruddha 2008; Zhang et al. 1999). 2

3 Fg 1. Schematc Dagram of Chemcal Asssted Ultrasonc Machne Materal and Methods Selected parameters and levels are shown n Table 2. For the desgn of experment orthogonal array L 27 s used and desgn s prepared by Mntab 6.7 software. The desgn of experment s shown n Table 3. All the experments are performed accordng to the desgn experment. MRR and TWR are calculated by the equaton 1 and equaton 2, n whch densty of work materal ρ s 8.3 gm/cm 3, W s ntal weght, W f fnal weght after processng, t s tme take n machnng. T ntal and T f weght of tool and ρ = Densty of D2 Steel 7.83 gm/cm 3, Densty of HC steel 7.85 gm/cm 3, Densty of HST steel 8.13 gm/cm 3 (Hasan et al. 2012; Hasao et al. 2008) MRR = W W f ρ X t 1000 (mm 3 /mn) (1) TWR = T T f ρ X t 1000 (mm3 /mn) (2) Surface roughness s measured s R a, t s the unversally recognsed and most used nternatonal parameter of roughness. It s the arthmetc mean of the absolute departure of the roughness profle from the mean lne. Table 2. Dfferent nput or controllable machnng parameters & ther levels Factor Levels Level 1 Level 2 Level 3 Concentraton (A) Abrasve (B) Al 2 O 3 +B 2 C SC+ B 2 C Al 2 O 3 + SC+ B 2 C Power Rate (C) Grt Sze (D) HF Acd (E) 0.5% 1% 1.5% Tool Materal (F) D2 Hgh-Carbon Steel Hgh-Speed Tool Steel 3

4 Table 3. Desgn of expermentaton (Orthogonal Array L27) and ther levels Tral Concent raton Type of Abrasve Power Rate Grt Sze HF Acd Tool Materal Al 2 O 3 +B 2 C % D Al 2 O 3 +B 2 C % HCS Al 2 O 3 +B 2 C % HSTS SC+B 2 C % D SC+B 2 C % HCS SC+B 2 C % HSTS Al 2 O 3 +SC+B 2 C % D Al 2 O 3 +SC+B 2 C % HCS Al 2 O 3 +SC+B 2 C % HSTS Al 2 O 3 + B 2 C % HCS Al 2 O 3 + B 2 C % HSTS Al 2 O 3 + B 2 C % D SC + B 2 C % HCS SC + B 2 C % HSTS SC+ B 2 C % D Al 2 O 3 +SC+B 2 C % HCS Al 2 O 3 +SC+B 2 C % HSTS Al 2 O 3 +SC+B 2 C % D Al 2 O 3 + B 2 C % HSTS Al 2 O 3 + B 2 C % D Al 2 O 3 + B 2 C % HCS SC + B 2 C % HSTS SC + B 2 C % D SC+ B 2 C % HCS Al 2 O 3 +SC+B 2 C % HSTS Al 2 O 3 +SC+B 2 C % D Al 2 O 3 +SC+B 2 C % HCS After machnng the MRR and TWR are calculated and SR s checked, machnng data s shown n Table 4. In whch MMR and TWR s calculated n mm 3 /mn and surface roughness n R a. 4

5 Table 4. Desgn of expermentaton (Orthogonal Array L27) and ther levels Tral Polycarbonate Bullet proof (UL-752) glass Acrylc Heat Resstant (BS-476) Glass MRR (mm 3 /mn) TWR (mm 3 /mn) (SR) Ra (Mcron) MRR (mm 3 /mn) TWR (mm 3 /mn) (SR) Ra (Mcron) Results and Dscusson In grey relaton analyss, data pre-processng s necessary to sequence scatter range. Data preprocessng s a process n whch orgnal sequence s transferred nto comparable sequence. The experment results are normalzed n the range between zero (0) and one (1). Dependng on output parameters, data pre-processng methodologes are adopted (Ln et al. 2002; Ln & Lee 2009; You et al. 2017). MRR s the governng output parameter n USM, whch decded the machnablty of work materal under delberaton. Larger-the-better characterstcs s used for MRR to normalze the orgnal sequence by equaton 3. X * ( k) X( K) MnX ( K) MaxX ( K) MnX ( K) (3) * K Where, X ( ) s the sequence after the data processng, X (K) s the comparablty sequence, K=1 and k=4 for MRR; = 1,2,3 27 for experment number 1 to 27. TWR and SR are the mportant measure of USM, these output parameters are represent the machnng accuracy under selected nput parameters (Patl & Patl 2016; Das et al. 2016). To get the optmum performance the Smaller-the-better characterstc has been preferred to normalze the orgnal sequence date by equaton 4. 5

6 X * ( K) MaxX ( K) X MaxX ( K) MnX ( K) ( K) (4) Where, X * ( K) s the sequence after the data processng, (K) s the comparablty sequence, K=2, K=5 for TWR and K=3, K=6 for SR; = 1,2,3 27 for experment number 1 to 27. ( ) s the X X X * K value after grey relatonal generaton, Mn X (K) and Max (K) are the smallest and largest value of X (K). After normalzed MRR, TWR and SR of PBPG UL-752 and AHRG BS-476 comparable sequence s shown n the Table 5. Now ( ) s the devaton sequence between reference sequence X 0 ( K ) and the comparablty 0 K * K sequence X ( ) (Ahmad et al. 2016). Devaton sequence s calculate by the equaton 5 and maxmum and mnmum dfference s found, K=1, 2 and 3 and = 1, 2, K) X ( K) X ( ) (5) 0( 0 K Table 5. The sequences of each performance characterstc after data processng Polycarbonate Bullet proof (UL-752) glass Acrylc Heat Resstant (BS-476) Glass Tral MRR TWR SR MRR TWR SR Reference Sequence

7 The devaton sequence table s shown n the Table 6, Maxmum ( Max ) and Mnmum ( Mn ) are obtaned and shown below. Max = ) = ) = ) = ) = ) = ) =1 10 (1 26 (2 02 (3 19 (4 26 (5 02 (6 Mn = ) = ) = ) = ) = ) = ) =0 26 (1 09 (2 23 (3 08 (4 After per-processng data, the next step n calculate the Grey relatonal coeffcent and Grey relaton grade wth the pre-processed data (Ln et al. 2009). It defne the relatonshp between deal and actual normalzed results. Grey relatonal coeffcent can be expressed as equaton 6 s shown below. 09 (5 19 (6 Mn Max ( K) ( K) Max 0 (6) Table 6. The devaton sequences Devaton Sequence 0(1) 0 (2) 0 (3) 0 (4) 0 (5) 0 (6) Where, ( ) s the devaton sequence of the reference sequence X 0 ( K ) and the comparablty 0 K sequence, s dstngushng or dentfcaton coeffcent. In ths calculaton =0.5 because all 7

8 parameters are gven equal preference (Ln 2012). The Grey relaton coeffcent for each experment of the L27 orthogonal array s calculated by usng equaton 6 and shown n Table 7. Table 7. The calculated Grey Relatonal Grade and ts order n the optmzaton process Expt. No. Grey Relatonal Coeffcent { (1)} { (2)} { (3)} { (4)} { (5)} { (6)} Grey Relaton Grade 1 m { (1) (2) (3) 6 (4) (5) (6)} After obtanng the Grey relaton coeffcent, the Grey relaton grade s obtaned by averagng the Grey relaton coeffcent correspondng to each performance characterstc and represent by (1), (2), (3) (4), (5) and (6) Equaton 7 (Manvanna et al. 2011) show the general formula of Grey relaton grade and equaton 8 s for three output parameters, shown n Table 7. 1 { ( K)} (7) n k n 1 1 { (1) (2) (3)} (8) 3 Ran k 8

9 The hgher value of Grey relaton grade s represent that the correspondng experment result s much closer to the deally normalzed value. Experment number 25 get the best multple performance characterstcs among the 27 experment because t have the hghest value of grey relaton grade. Now the expermental desgn s orthogonal, t s possble to separate out the effect of each parameters on the bass of Grey relaton grade. Mean of Grey relaton grade s calculated for level 1, 2 and 3 by averagng the Grey relaton grade of the experment 1to 9, 10 to18 and 19 to 27 are shown n Table 8. The mean of Grey relaton grade for abrasve, power rate, grt sze, HF acd and tool materal are calculated n same manner. The total mean of Grey relaton grade for 27 experment s also shown n the Table 8. *Level for optmum grey relatonal grade. Optmum level parameters are fnd out from response table and shown n the Fg.2. Larger value of Grey relaton grade s closer to the deal value. Therefore, the optmum parameters settng for hgher MRR and lower TWR and SR are A 3 B 3 C 2 D 3 E 1 F 3 Table 8 Response Table for the Grey Relatonal Grade Symbol Machnng Grey Relaton Grade Man Effect Rank Parameters Level 1 Level 2 Level 3 (Max- Mn) A Concentraton B Abrasve C Power Rate D Grt Sze E HF Acd F Tool Materal Total men value of the Grey relatonal Grade = m Fg. 2 Effect of USM parameters on the multple performance characterstcs. Furthermore, analyss of varance (ANOVA) s performed on Grey relaton grade to acheve contrbuton of each nput parameter affectng the output parameters. ANOVA for Grey relatonal grade s shown n Table 9. In addton, F-test s also used to fnd out the percentage contrbuton of each parameters. From Table 9 t s clear that materal of tool have the sgnfcant role n the machnng whch have 30% contrbuton, 25% contrbuton of concentraton, 21% contrbuton of grt sze, 9% 9

10 contrbuton of abrasve, 4% contrbuton of HF acd and 3% contrbuton of power rate n the machnng of PBPG UL-752 and AHRG BS-476. Table 9 ANOVA of Grey relaton grade Parameter Degree of Freedom Sum of Squares Mean Squares F Raton Percentage Contrbuton Concentraton (A) % Abrasve (B) % Power Rate (C) % Grt Sze (D) % HF Acd (E) % Tool Materal (F) % Error % Total After gettng the optmum parameters for machnng the experment s performed by those nput settng (A 3 B 3 C 2 D 3 E 1 F 3 ). Fg.3 show the Scannng electron mcroscope (SEM) mages of PBPG UL-752 machnng settng A 1 B 1 C 1 D 1 E 1 F 1, In whch machnng by USM s performed and some crack are also found on the work surface. In other hand n Fg. 4 the USM machnng of PBPG UL-752 s performed by optmum parameters whch are found by Grey relatonal analyss A 3 B 3 C 2 D 3 E 1 F 3, there s smoother and crack free surface. Smlarly, n Fg.5 show the Scannng electron mcroscope (SEM) mages of AHRG BS-476 machnng settng A 1 B 1 C 1 D 1 E 1 F 1, n whch machnng by USM s performed and some crack are also found on the work surface. In other hand n Fg. 6 the USM machnng AHRG BS-476 s performed by optmum parameters whch are found by Grey relatonal analyss A 3 B 3 C 2 D 3 E 1 F 3, there s smoother and crack free surface. Fg. 3 SEM mage of PBPG UL-752 A 1 B 1 C 1 D 1 E 1 F 1 experment 10

11 Fg. 4 SEM mage of PBPG UL-752 A 3 B 3 C 2 D 3 E 1 F 3 optmum Grey relatonal analyss Fg. 5 SEM mage of AHRG BS-476A 1 B 1 C 1 D 1 E 1 F 1 experment Fg. 6 SEM mage of AHRG BS-476A 3 B 3 C 2 D 3 E 1 F 3 optmum Grey relatonal analyss 11

12 MRR and TWR are also compared between optmum Grey relatonal analyss A 3 B 3 C 2 D 3 E 1 F 3 and A 1 B 1 C 1 D 1 E 1 F 1 nput parameters. It observed that optmum parameters (A 3 B 3 C 3 D 3 E 1 F 3 ) gves 73.02% mproved MRR wth comparson of A 1 B 1 C 1 D 1 E 1 F 1 USM experment settng. TWR s decreased by 37.25%. It s evdent from SEM mage, optmum parameters settng also gve the better surface roughness whch s 43.33% mproved. Fg 7 shown the percentage contrbuton of optmum Grey relatonal analyss parameters. Fg. 7 Percentage contrbuton of factor on Grey Relatonal Grade Confrmaton test s carred out to verfy the mprovement of performance characterstcs n machnng of PBPG UL-752 and AHRG BS-476 by USM. The optmum parameters are shown n the Table 10. The estmated Grey relatonal grade ˆ usng the optmal level of machnng parameters can be calculated by usng equaton 9 (Meena & Azad 2012; Sngh et al. 2004; Sreenvasulu & srnvasarao 2012). n { ˆ } (9) m 1 Table 10. Improvement n Grey relatonal grade wth optmzed USM machnng parameters Condton Descrpton Machnng Parameters n Frst tral of OA m Optmal Machnng Parameters Grey Theory Predcton Desgn PBPG UL-752 Grey Theory Predcton Desgn AHRG BS-476 Level A 1 B 1 C 1 D 1 E 1 F 1 A 3 B 3 C 2 D 3 E 1 F 3 A 3 B 3 C 2 D 3 E 1 F 3 MRR (mm 3 /mn) TWR (mm 3 /mn) SR (mcron) Grey Relatonal Grade ` Improvement n Grey relatonal grade = Where, m s the total mean of Grey relatonal grade, s mean of the Grey relatonal grade at optmum level and n s the number of parameters that sgnfcantly affect multple-performance characterstcs. It s clearly show that the multple-performance characterstcs n USM process s greatly mproved through ths study. 12

13 Concluson The optmum machnng parameters are dentfy by Grey relatonal grade for multple performance characterstcs that s MRR, TWR and SR. Ths expermental research paper presented the multobjectve optmzaton of USM machnng parameters of polycarbonate bullet proof UL-752 and acrylc heat resstant BS-476 glass for drllng applcaton by Grey relatonal analyss method. Followng concluson are conclude from the expermentaton analyss. (1) Concentraton of abrasve slurry, concentraton and grt sze of abrasve play the sgnfcant role for optmum output performance parameters. (2) ANOVA of Grey relatonal grade for multple performance characterstcs reveals that the concentraton have the sgnfcant role n the MRR. (3) Based on SEM mages, t s evdent that optmum parameter mprove the surface roughness and gve better smooth surface. (4) PBPG UL-752 have mprovement n MRR, TWR and SR s 75.58%, 45.34% and 34.18% respectvely, based on confrmaton test. (5) AHRG BS-476 have mprovement n MRR, TWR and SR s 61.24%, 31.46% and 23.85% respectvely, based on confrmaton test. (6) It proof that, the performance characterstc of USM process lke MRR, TWR and SR are mproved together by usng the Grey relatonal study and the effectveness of ths method s effectvely recognsed by authentcaton experment. References Kurakose, S., Patowar, P.K. & Bhatt. J. (2017). Machnablty study of Zr-Cu-T metallc glass by mcro hole drllng usng mcro-usm. Journal of materal processng technology. 240; Wang, J., Feng, P., Zhang, J., Ca, W. & Shen. H. (2016). Investgatons on the crtcal feed rate guaranteeng the effectveness of rotary ultrasonc machnng. Ultrasonc. 74; L, G., Yu, Z., Song, J., L. C., L. J. & Natsu, W. (2016). Materal removal mode of quartz crystals by Mcro USM. Proceda CIRP. 42; Ln, Y.C., Hung, J.C. Chow, H.M. Wang, A.C. & Chen, J.T. (2016). Machnng characterstcs of a hybrd process of EDM n gas combned wth ultrasonc vbraton and AJM. Proceda CIRP. 42; Kharay, A.B.E. (1990). Assessment of some dynamc parameters for the ultrasonc machnng process. Wear. 137; Cho, J.P., Jeon, B.H. & Km, B.H. (2007). Chemcal-asssted ultrasonc machnng of glass. Journal of materal processng technology. 191; Guzzo, P.L., Shnohara, A.H. & Rasan, A.A. (2004). A comparatve study on ultrasonc machnng of hard and brttle materals. Journal of materal processng technology. 26; Hofy, H.E.L. Advanced Machnng Process. Second ed. Mcgraw- Hll, Egypt; Jatnder, K. & Khamba, J.S. Modelng. (2010).The materal removal rate n ultrasonc machnng of ttanum usng dmensonal analyss. Internatonal journal of advanced manufacturng technology. 48;

14 Lee, T.C. & Chan, C.W. (1997). Mechansm of ultrasonc machnng of ceramc compostes. Journal of materal processng technology. 71; Sngh, K. & Ahuja, I.P.S. (2014). Ultrasonc machnng processes revew paper. Internatonal journal for mult-dscplnary engneerng and busness management. 2; Sngh, K. & Sngla, V.K. (2014). Fnte element analyss of ultrasonc machne tool. Internatonal journal of engneerng research & technology. 3; Sngh, R. & Khanba, J.S. (2008). Comparson of slurry effect on machnng characterstcs of ttanum n ultrasonc drllng. Journal of materal processng technology. 197; Sngh, R. & Khamba, J.S. (2007) Taguch technque for modellng materal removal rate n ultrasonc machnng of ttanum. Materals scence and engneerng ; Thoe, T.B., Aspnwall, D.K. & Wse, M.L.H. (1998). Revew on ultrasonc machnng. Journal of machne tool and manufacture. 38; Vnod. Y. & Anruddha, D. (2008). Desgn of horn for rotary ultrasonc machnng usng the fnte element method. Internatonal journal of advanced manufacturng technology. 39; Zhang, Q.H., Zhang, J.H., Ja, Z.X. & Sun, J.L. (1999). Materal removal rate analyss n the ultrasonc machnng of engneerng ceramcs. Journal of materal processng technology. 88; Hasan, H., Tabatabae., S.A. & Amr, G. (2012). Grey relatonal analyss to determne the optmum process parameters for open end sprnnng yarns. Journal of engneerng fbers and fabrcs. 7 (2); Hasao, Y.F., Tarng, Y.S. & Huang, W.J. (2008). Optmzaton of plasma are weldng parameters by usng the Taguch method wth the Grey relatonal analyss. Materals and manufacturng processes. 23; Ln, C.L., Ln, J.L. & Ko, T.C. (2002) Optmsaton of EDM process based on the orthogonal array wth fuzzy logc and grey relatonal analyss method. Internatonal journal of advanced manufacturng technology. 19; Ln, Y.C. & Lee, H.S. (2009). Optmzaton of machnng parameters usng magnetc-forceasssted EDM on grey relatonal analyss. Internatonal journal of advanced manufacturng technology. 42; You, M.L., Shu, C.M., Chen, W.T. & Shyu, M.L. (2017). Analyss of cardnal grey relatonal grade and grey entropy on achevement of ar polluton reducton by evaluatng ar qualty trend n Japan. Journal of cleaner producton. 142 (4); Patl, P.J. & Patl, C.R. (2016). Analyss of process parameters n surface grndng usng sngle objectve Taguch and mult-objectve grey relatonal grade. Perspectve n Scence. 8; Das, B., Roy, S., Ra, R.N. and Saha, S.C. (2016). Applcaton of greay fuzzy logc for the optmzaton of CNC mllng parameters for Al-4.5% Cu-TC MMCs wth mult performance characterstcs. Engneerng scence and technology, an nternatonal journal. 19(2); Ahmad, N., Kamal, S., Raza, Z.A. & Hussan, T. (2016). Mult-response optmzaton n the development of oleo-hydrophobc cotton fabrc usng Taguch based grey relatonal analyss. Appled surface scence. 367;

15 Ln, Y.H., Lee, P.C. & Chang, T.P. (2009). Practcal expert dagnoss modal based on the grey relatonal analyss technque. Expert system wth applcatons. 36; Ln, H.L. (2012). The use of the Taguch method wth grey relatonal analyss and a neural network to optmze a novel GMA weldng process. Journal of ntellgent manufacturng. 23(5); Manvannan, S., Prasanna, S. & Aramugam, R. (2011) Mult-objectve optmzaton of flat plate heat snk usng Taguch based grey relatonal analyss. Internatonal journal of advanced manufacturng technology. 52; Meena, V.K. & Azad, M.S. (2012). Grey relatonal analyss of mcro EDM machnng of T- 6Al-4V alloy. Materal and manufacturng processes. 27; Sngh, P.N., Raghukandan, K. & Pa, B.C. (2004). Optmzaton by grey relatonal analyss of EDM parameters on machnng Al-10%SCp compostes. Journal of materal processng technology ; Sreenvasulu, R. & Srnvasarao,C., Applcaton of Grey relatonal analyss for surface roughness and roundness error n drllng of AL 6061 alloy. Internatonal journal of lean thnkng. 3(2);