Process parameters optimization on machining force and delamination factor in milling of GFRP composites using grey relational analysis

Transcription

1 Indan Journal of Engneerng & Materals Scences Vol., June 05, pp. -0 Process parameters optmzaton on machnng force and delamnaton factor n mllng of GFRP compostes usng grey relatonal analyss M P Jenarthanan a * & N Naresh b a School of Mechancal Engneerng, SASTRA Unversty, Thanjavur 6 40, Inda b Department of Mechancal Engneerng, Sree Vdyankethan Engneerng College, Trupat 57 0, Inda Receved June 04; accepted 0 December 04 In ths study, the optmzaton of process parameters for mllng of glass fber renforced polymer (GFRP) compostes usng grey relatonal analyss has been nvestgated. Experments are conducted usng helx angle, spndle speed, feed rate, depth of cut and fber orentaton angle as typcal process parameters. The grey relatonal analyss (GRA) s adopted to obtan grey relatonal grade for mllng process wth multple characterstcs namely machnng force and delamnaton factor. Analyss of varance (ANOVA) s performed to get the contrbuton of each parameter on the performance characterstcs and t s observed that fber orentaton angle and feed rate are the most sgnfcant process parameters that affect the mllng of GFRP compostes. The expermental results reveal that, the helx angle of 5 o, spndle speed of 000 rpm, feed rate of 500 mm/mn, depth of cut of mm and fber orentaton angle of 5 o s the optmum combnaton for lower machnng force and lower delamnaton factor. The expermental results for the optmal settng show that there s consderable mprovement n the process. Keywords: GFRP, End mllng, GRA, Machnng force, Delamnaton factor GFRP compostes play a crucal role n aerospace ndustry as they mnmze the arcraft weght and consequently the costs ncurred upon, and also be used n automotve and sea vehcles ndustry. It s an advanced polymerc matrx composte materal and t s consdered to be a feasble alternatve to engneerng materals. They have superor propertes lke corroson resstance, hgh specfc strength, hgh stffness, low thermal expanson coeffcent, superor rgdty, hgh dampng, hgh fracture toughness, and resstance to chemcal and mcrobologcal attacks,. GFRP s are dffcult to machne due to splnterng and delamnaton of fber, and these components are largely made near net shape to achevng contour shape accuracy. Mllng composte materals are sgnfcantly affected by the tendency of these materals to delamnate under the acton of machnng forces,.e., cuttng force, feed force and depth force 4,5. The machnng force and delamnaton factor have been dentfed as qualty attrbutes and are assumed to be drectly related to performance of machnng process, productvty and producton costs. Machnng force play a key role n analyzng the machnng process of FRPs. The value of machnng force n the work-pece s determned usng the equaton *Correspondng author (E-mal: jenarthanan@mech.sastra.edu) F m = F + F + F Generally, machnng force x y z ncreases wth ncrease n feed rate and decreases wth ncrease n cuttng velocty 6. Evaluaton of machnng parameters of hand layup GFRP related to machnng force was carred out by Davm et al. 7 on mllng usng cemented carbde (K0) end mll. Mohan et al. 8 analyzed the nfluence of machnng parameters on cuttng force durng drllng of GFRP wth the help of a commercally avalable software package MINITAB4. Machnng of GFRP compostes s a crtcal operaton because of occurrence of fber delamnaton, fber/resn pull out, surface roughness of machned surface and walls, matrx burnng, chppng, spallng, etc. Among all these defects, delamnaton s most crtcal damage as strength of the polymer matrx composte product fabrcated gets mpared 9,0. It s mportant to choose the best machnng parameters for achevng optmum performance characterstcs for any machnng process. The desred machnng parameters are usually selected wth the help of referred handbooks, past experence and varous trals. However, the selected machnng parameters may not be optmal or near optmal machnng parameters. Taguch s parameter desgn s one of the mportant tools for robust desgn, whch offers a systematc approach for

2 4 INDIAN J. ENG. MATER. SCI., JUNE 05 parameters optmzaton n terms of performance, qualty and cost -5. Taguch technque had been appled to optmze surface roughness and delamnaton factor n use of T-Namte carbde K0 end mll, sold carbde K0 end mll and tpped carbde K0 end mll on GFRP composte materal, and they reported that T-Namte coated carbde end mll and tpped carbde end mll produces less damage on GFRP composte materal than the sold carbde end mll. Jule Zhang et al. presented a study of the Taguch desgn applcaton to optmze surface qualty n a CNC face mllng operaton. Davm attempted to study the nfluence of cuttng condtons on surface roughness durng turnng by Taguch desgn of experments and regresson analyss. Davm and Resand 4 evaluated the cuttng parameters (cuttng velocty and feed rate) under the surface roughness, and damage n mllng lamnate plates of carbon fber renforced plastcs (CFRPs) by Taguch method. Ghan et al. 5 also appled Taguch optmzaton methodology to optmze cuttng parameters n end mllng of hardened steel AISI H wth TN coated P0 carbde nsert tool under semfnshng and fnshng condtons of hgh speed cuttng. Thus, Taguch methodology can be effectvely used to optmze process parameters for sngle performance characterstc only. However, the optmzatons of multple performance characterstcs fnd more applcatons. Grey theory can provde an effcent soluton to the uncertanty n mult-nput and dscrete data problems. It had been effectvely appled to optmze the mult response processes through the settng of process parameters 6-8. Grey relatonal analyss can be used to fnd out the relatonshp of the reference sequence wth other sequences or the relatonal degree exstng between the varatons of any two dfferent sequences. The advantage of ths method s that many factors can be analysed usng less data 7. The approach about grey theory and ts applcatons are llustrated n Refs 9-. The varous authors reported that the qualty characterstcs of the composte materal strongly dependent on type and orentaton of fber, cuttng parameters and tool geometry. Machnablty study for surface roughness and specfc cuttng pressure was nvestgated on hand lad up GFRP materals by Davm and Mata,4 usng polycrystallne damond (PCD) and cemented carbde (K5) cuttng tools. It reveals that the PCD tool performs well compared to cemented carbde (K5) tool n terms of surface roughness and specfc cuttng pressure. Fber orentaton s a key factor that determnes the surface ntegrty of a machned surface and 90 s a crtcal angle, beyond whch a severe subsurface damage wll occur. If the fber orentaton angle s greater than 90, the three dstnct deformaton zones appear namely chppng, pressng and bouncng 5. Palankumar et al. 6 have assessed the nfluence of machnng parameters on surface roughness n machnng GFRP compostes, he concluded that the feed rate nfluences more on surface roughness followed by cuttng speed. Nesel et al. 7 reported that the tool nose radus s a domnant factor on the surface roughness n turnng of AISI steel. The attempt to correlate the drllngnduced damage wth the drllng parameters of undrectonal GFRP composte lamnates was made by Sngh and Bhatnagar 8. From the lterature, t has been observed that Taguch methodology can be appled for analysng the best process parameters for sngle performance characterstcs only,.e. surface fnsh, delamnaton factor, specfc cuttng pressure, tool nose radus, one parameter at a tme only, whereas grey relatonal analyss can effectvely be used for analyzng mult-performance characterstcs ncorporatng the above all parameters at a tme. The man objectve of present work s to optmze the machnng characterstcs,.e., machnng force and delamnaton factor on mllng of GFRP compostes usng grey relatonal analyss (GRA). GRA converts the multple performance characterstc n to one numercal score called gray relatonal grade. Based on gray relatonal grade, the optmal level of parameters can be obtaned. The Taguch s L 7 orthogonal array s utlzed for expermental nvestgaton on CNC machnng centre. Analyss of varance (ANOVA) s also performed to nvestgate the most nfluencng parameters on the machnng force and delamnaton factor. Expermental Procedure A CNC mllng (Hartford) wth 5 kw spndle power and maxmum spndle speed of 000 rpm s used to perform the machnng operatons. A schematc dagram of the expermental set-up used n ths study s shown n Fg.. Glass fber renforced plastcs (GFRP) composte plates made by hand lay-up method are used for these experments. GFRP plates are of 70 mm 70 mm mm thck wth lay-up wth desred fber orentaton (5, 60 and 05 ) are used for the machnng operatons and are shown n

3 JENARTHANAN & NARESH : MILLING OF GFRP COMPOSITES 5 Fg.. The sold carbde end mll of 5 mm dameter wth dfferent helx angles (5, 5, and 45 ) are used for the machnng operatons and are shown n Fg.. The machnng performance s evaluated n terms of machnng force (F m ) and delamnaton factor. The force measurement was carred out usng a Kstler dynamometer. The data acquston was carred out by approprate software called Dynawarekstler. The measurement of delamnaton was done usng shop mcroscope Mtutoyo TM-500 and the maxmum wdth of damage n µm was measured as shown n Fg. 4. The measurement of the maxmum wdth of damage (W max ) suffered by the materal, the damage normally assgned by delamnaton factor (F d ) was determned. Ths factor s defned as the quotent between the maxmum wdth of damage (W max ), and the wdth of cut (W). The value of delamnaton factor (F d ) s obtaned by the followng Eq. (); W F d = W max () W max beng the maxmum wdth of damage n µm and W be the wdth of cut n µm selected, and are shown n Table. In full factoral desgn, the number of expermental runs exponentally ncreases as the number of factors as well as ther level ncreases. Ths results huge expermentaton cost and consderable tme 9. So, n order to compromse these two adverse factors and to search the optmal process condton through a lmted number of expermental runs, Taguch desgn of experments has been selected to optmze the multple performance characterstcs. The desgn of experments usng orthogonal array s most effcent Process control parameters (Cuttng tool geometry) Table Machnng parameters and ther levels Levels Unts Symbol (deg) A Helx angle Spndle Speed (rpm) B Feed rate (mm/mn) C Depth of cut (mm) D Fber Orentaton Angle (deg) E Desgn of experments To perform the expermental desgn, three levels of machnng parameters,.e., helx angle, spndle speed, feed rate, depth of cut and fber orentaton angle are Fg. Sold carbde end mll wth dfferent helx angles Fg. Expermental set-up Fg. Mlled GFRP plates wth sold carbde end mlls Fg. 4 Dagram of the measurement of the wdth of maxmum damage wth a shop mcroscope Mtutoyo TM 500

4 6 INDIAN J. ENG. MATER. SCI., JUNE 05 when compared to many other statstcal desgns. The mnmum number of experments that are requred to conduct the Taguch method can be calculated based on the degrees of freedom approach. Frst, the degrees of freedom must be calculated before an orthogonal array s selected. It can be calculated by the followng Eq. () No of var ables () = N=+ ( L ) where N s the number of degrees of freedom and L s the number of levels of machnng nput varables. Accordngly, Taguch based L 7 orthogonal array s selected. The expermental combnatons of the machnng parameters usng the L 7 orthogonal array and performance results are presented n Table. Machnng force and delamnaton are the performance characterstcs. Therefore, the both machnng force and delamnaton factor are the lower-the-better performance characterstcs. Grey relatonal analyss (GRA) In ths secton, the use of the orthogonal array wth the grey relatonal analyss for determnng the optmal machnng parameters s reported step by step. Optmal machnng parameters wth consderatons of the multple performance characterstcs are obtaned and verfed. Data preprocessng Grey data processng must be performed before grey correlaton coeffcents are calculated. A seres of varous unts must be transformed to be dmensonless. In order to fnd grey relatonal grade usually, each seres s normalzed by dvdng the data n the orgnal seres by ther average. Let the orgnal reference sequence and sequence for comparson be represented as x (k) and y (k), =,.m; k=,,.n, respectvely, where m s the total number of experments to be consdered, and n s the total number of observed data. Data preprocessng converts the orgnal sequence to a comparable sequence. Several methodologes of preprocessng data can be used n GRA, dependng on the characterstcs of the orgnal sequence. If the target value of the orgnal sequence s the-larger-thebetter, then the orgnal sequence s normalzed as follows; y -mn y X (k) = max y -mn y () If the purpose s the-smaller-the-better, then the orgnal sequence s normalzed as follows; max y - y X (k) = max y -mn y (4) Grey relatonal coeffcents and Grey relatonal grades Followng the data preprocessng, a grey relatonal coeffcent can be calculated usng the pre-processed sequences. The grey relatonal coeffcent s defned as follows; +ξ ψ ( mn max k ) = ( )+ξ o k max (5) Where, o = xo - x ( k ) = dfference of absolute value xo and x ( k ) ; ξ = the dstngushng coeffcent 0 ξ ; mn mn mn j ε k xo x = = The smallest value of = ε = max max 0 ;and max j k xo x the largest value of 0. The grey relatonal coeffcent values are used to fnd the grey relatonal grade. The grey relatonal grade for each expermental run can be obtaned by accumulatng the grey relatonal coeffcent of each qualty characterstc. The average grey grade for the th expermental run for all n responses s gven by; Y = n x k= (6) n where n s the number of process responses and x (k) s the grey relatonal coeffcent of k th response n th experment. The hgher value of grey relatonal grade corresponds to ntense relatonal degree between the reference sequence x o (k) and the gven sequence x (k). The reference sequence x o (k) represents the best process sequence; therefore, hgher grey relatonal grade means that the correspondng parameter combnaton s closer to the optmal. The optmum level of the process parameters s the level wth the hghest grey relatonal grade. In Eq. (5) for fndng grey relatonal coeffcent, the value of s taken as 0.5 snce both the responses are of equal weght.

5 JENARTHANAN & NARESH : MILLING OF GFRP COMPOSITES 7 Results and Dscusson Table Expermental layout usng an L 7 orthogonal array and performance results Process parameters Responses Exp. No. A B C D E F m (N) F d Optmal parameter combnaton The expermental results for the machnng force and delamnaton factor are lsted n Table. Bascally, the both machnng force and delamnaton factor belongs to the smaller-the-better methodology that n Eq. () whch s employed for data preprocessng. The values of the machnng force and delamnaton factor are set to be the reference sequence x (k), k=,. Moreover, the results of twenty seven experments are the comparablty sequences y (k), =,.7, k=,. Table lsts all of the sequences after mplementng the data preprocessng usng Eqs. () and (). Then the devaton sequences, o (k)= x o - x ( k ) max and mn for =-7, k=, can be calculated. The dstngushng coeffcent can be substtuted for the grey relatonal coeffcent n Eq. (5). If all the performance characterstcs have equal weghtage, ξ s set to be 0.5. The grey relatonal grade s calculated based on the Eq. (6). Table 4 lsts the grey relatonal coeffcents and the grade for all twenty seven comparablty sequences. The hgher grey relatonal grade represents that the correspondng expermental result s closer to the deally normalzed value Table The data preprocessng of each ndvdual qualty characterstc Experment run Machnng force, F m (N) F d Reference sequence

6 8 INDIAN J. ENG. MATER. SCI., JUNE 05 Ths nvestgaton employs the response table of the Taguch method to calculate the average grey relatonal grades for each factor level, as llustrated n Table 5 and s represented graphcally n Fg. 5. Snce the grey relatonal grades represent the level of correlaton between the reference and the comparablty sequences, the larger grey relatonal grade means the comparablty sequence exhbtng a stronger correlaton wth the reference sequence. Based on ths study, a combnaton of the levels can be selected so that t can provde the largest average response. In Table 5, the combnaton of A, B, C, D and E shows the largest value of the grey Table 4 The grey relatonal coeffcent and grey relatonal grade of each ndvdual qualty characterstc Exp. run Grey relatonal coeffcent Machnng force, F d F m (N) Grey relatonal grade relatonal grade for the factors A, B, C, D and E respectvely. Therefore, t s observed that the helx angle of 5 o, spndle speed of 000 rpm, feed rate of 500 mm/mn, depth of cut of mm, and fber orentaton angle of 5 o s the optmal parameter combnaton of the mllng of GFRP compostes. Analyss of varance (ANOVA) Analyss of varance (ANOVA) s a method of apportonng varablty of an output to varous nputs. The purpose of the statstcal ANOVA s to nvestgate whch desgn parameter sgnfcantly affects the performance characterstc. Ths s accomplshed by separatng the total varablty of the grey relatonal grade, whch s measured by the sum of the squared devatons from the total mean of the grey relatonal grade, nto contrbutons by each machnng parameter and the error. Frst, the total sum of the squared devatons SS T must be calculated usng Eq. (7); (7) m SS T = ( j m) j= where m s the number of experments n the orthogonal array, η j s the grey relatonal grade for the j th experment and η m s the total mean of the grey relatonal grade. The total sum of the squared devatons SS T s decomposed n to the sum of the squared devatons SS d due to each machnng parameter and the sum of the squared error SS e. The percentage contrbuton (P) by each of the machnng parameter n the total sum of the squared devatons SS T can be used to evaluate the mportance of the machnng parameter change on the performance characterstc. In addton, the Fsher s F-test can also be used to determne whch machnng parameters have a sgnfcant effect on the performance Table 5 Response table for the grey relatonal grade Grey relatonal grade Symbol Process parameters Level- Level- Level- A Helx angle 0.67* B Spndle speed 0.688* C Feed rate 0.669* D Depth of cut 0.69* E Fber orentaton angle 0.704* Mean grey relatonal grade = Fg. 5 Effect of mllng parameters on mult-performance characterstcs

7 JENARTHANAN & NARESH : MILLING OF GFRP COMPOSITES 9 Factors characterstc. Usually, the change of the machnng parameters has a sgnfcant effect on performance characterstc when F s large. The analyss s carred out for the level of sgnfcance of 5% (the level of confdence s 95%). Table 6 shows the result of ANOVA for the response characterstcs of GFRP compostes. From Table 6, t s observed that the fber orentaton angle (percentage contrbuton, P = 50.89%) s the most sgnfcant machnng parameter and feed rate (P = 5.6%) s the next sgnfcant parameter affectng the multple performance characterstcs for GFRP compostes. Confrmaton test Confrmaton test has been carred out to verfy the mprovement of performance characterstcs whle machnng of GFRP compostes. Optmum parameters are selected for the confrmaton test as gven n Table 5. The estmated grey relatonal grade usng the optmal level of the machnng parameters can be calculated as: Sum of square (SS) ( ) q ˆγ =γ m + γ = j -γm Table 6 Results of the ANOVA Degree of freedom Mean square (MS) F-test % Contrbuton A B C D E Error Total Intal machnng parameters Levels Machnng force, F m (N) Delamnaton factor, F d Grey relatonal grade Table 7 Results of confrmaton test A B C D E Improvement n grey relatonal grade = Optmal machnng parameters Predcton Experment A B C D E A B C D E (8) where, γ m s the total mean of the grey relatonal grade, γ j s the mean of the grey relatonal grade at the optmum level and q s the number of machnng parameters that sgnfcantly affects the multple performance characterstcs. The obtaned process parameters, whch gve hgher grey relatonal grade, are presented n Table 7. The predcted machnng force, delamnaton factor and grey relatonal grade for the optmal machnng parameters are obtaned usng the Eq. (8) and also presented n Table 7. Table 6 also shows the comparson of expermentally obtaned machnng force and delamnaton factor (tral 8 of the OA) and expermentally obtaned machnng force and delamnaton factor at optmum machnng process parameters. It can be seen that the overall performance of machnng of GFRP compostes has been mproved. Though the machnng force has been reduced from 4. N to 5.4 N and the delamnaton factor reduced from.049 to.0. Conclusons In ths work, the machnng characterstcs of GFRP composte plates made by hand lay-up are thoroughly analyzed. From ths study, the followng conclusons are drawn: () The optmal parametrc combnaton for lower machnng force and lower delamnaton factor were found to be helx angle at 5 o, spndle speed at 000 rpm, feed rate at 500 mm/mn, depth of cut at mm, and fber orentaton angle at 5 o. () The ANOVA of grey relatonal grade for multple performance characterstcs revealed that the work-pece fber orentaton angle and feed rate are the most sgnfcant parameters. () It s clear from the above study that optmzaton of the complcated multple performance characterstcs can be greatly smplfed through Taguch and grey relatonal analyss approach. It s shown that the performance characterstcs of the mllng process such as machnng force and delamnaton factor are mproved by usng the method proposed by ths study. The effectveness of ths approach has been successfully establshed by confrmaton test. Confrmaton test result proves that there s a remarkable mprovement n the grey relatonal grade value from to , when machnng s done wth the optmal parametrc combnaton. The results of these experments have hgh practcal relevance. The fndngs allow mllng processes to be confgured such that delamnaton s largely avoded, meanng consderable tme and cost savngs for the manufacture of composte components.

8 0 INDIAN J. ENG. MATER. SCI., JUNE 05 References Shyha I, Soo S L, Aspnwall D & Bradley S, J Mater Process Technol, 0 (00) Sngh A P & Sharma M, Proceda Eng, 5 (0) Shekh-Ahmad J Y, Machnng of Polymer Compostes, (Sprnger Scence), Sreejth P S, Krshnamurthy R, Malhota S K & Narayanasamy K, J Mater Process Technol, 04 (000) Ferrera J R, Coppn N L & Mranda G W A, J Mater Process Technol, 9 (999) Kopf A, Festrtzer S & Uder K, Int J Refract Met Hard Mater, 4 (006) Davm J P, Res P & Concecao A C, Compos Struct, 64 (004) Mohan N S, Ramachandra A & Kulkarn S M, J Compos Struct, 7 (005) Davm J P, Rubo J C & Abrao A M, Compos Sc Technol, 67 (007) Jenarthanan M, Jeyapaul R & Naresh N, Multdscplne Modelng Mater Struct, 9 (0) Praveen Raj P & Elaya Perumal A, J Eng Sc Technol Rev, (00) Jule Zhang Z, Joseph Chenb C & Danel Krby E, J Mater Process Technol, 84 (007) -9. Davm J P, J Mater Process Technol, 6 (00) Davm J P & Pedro Resand P, J Mater Process Technol, 60 (005) Ghan J A, Choudhury I A & Hassan H H, J Mater Process Technol, 45 (004) Bala Murugan G, Bswanath M & Sukamal G, Int J Adv Manufact Technol, 45 (009) Deng J, J Grey Syst, (989) Ln J L, Wang K S, Yan B H & Tarng Y S, J Grey Syst, () (999) Ahlan C, Kumanan S & Svakumaran N, Adv Prod Eng Manage, 5() (00) Hakan Aydn, Al Bayram, Ugur Esme, Ygt Kazancoglu & Onur Guven, Mater Technol, 44(4) (00) 05-. Gopalakannan S, Senthlvelan T & Ranganathan S, J Sc Ind Res, 7 (0) Naresh N, Jenarthanan M P & Har Prakash R, Multdscplne Modelng Mater Structs, 0 (04) Davm J P & Mata F, J Mater Process Technol, 70 (005) Davm J P & Mata F, Mater Des, 8 (007) Wang X M & Zhang L C, Int J Mach Tools Manuf, 4 (00) Palankumar K, Karunamoorthy L & Karthkeyan R, Mater Des, 7 (006) Nesel S, Yaldzand S & Turkes E, Measurement, 44 (0) Sngh I & Bhatnagar N, Int J Adv Manuf Technol, 7 (006) Datta S, Bandyopadhyay A & Pal P K, Int J Adv Manuf Technol, 9 (008) 6-48.