Analysis Of Injection Moulding Process Parameters

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1 Analysis Of Injction Moulding Procss Paramtrs Mr. M.G. Rathi Assistant Profssor, partmnt of Mchanical Enginring, Govrnmnt Collg of Enginring Aurangabad, (MS), India. Mr. Manoj amodar Salunk Studnt, partmnt of Mchanical Enginring, Govrnmnt Collg of Enginring Aurangabad, (MS), India. Abstract In this study, analysis of injction moulding procss paramtrs was carrid out to minimiz short shots. Optimum lvl of factors ar dtrmind by OE tchniqu of Taguchi and th analysis of varianc (ANOVA) mthods. For this study CPVC spcimns wr tstd. trmination of optimum machin sttings was basd on S/N ratios. According to rsults mould closing spd had significant ffct on quality charactristic. Mould prssur and Injction prssur had no significant ffct. 1. Introduction Plastic injction molding uss plastic in th form of pllts or granuls as a raw matrial. It is thn hatd until a mlt is obtaind. Thn th mlt is injctd into a mould whr it is allowd to solidify to obtain th dsird shap. Th mould is thn opnd and th part is jctd. Th procss paramtrs such as cycl tim, fill tim, cooling tim, injction tim, injction spd, injction prssur, holding prssur, mlting tmpratur, mould tmpratur and so on nd to b optimizd in ordr to produc finishd plastic parts with good quality. Various studis hav bn conductd to improv and optimiz th procss, so as to obtain high quality parts producd on a wid rang of commrcial plastic injction molding machins. [1] Th Taguchi mthod is a wll-known tchniqu that provids a systmatic and fficint mthodology for procss optimization. It has bn widly usd for product dsign and procss optimization worldwid. [2] This is du to th advantags of th dsign of xprimnt using Taguchi s tchniqu, which includs simplification of xprimntal plan and fasibility of study of intraction btwn diffrnt paramtrs. Lssr numbr of xprimnts is rquird in this mthod. As a consqunc, tim as wll as cost is rducd considrably. Taguchi proposs xprimntal plan in trms of orthogonal array that givs diffrnt combinations of paramtrs and thir lvls for ach xprimnt. According to this tchniqu, th ntir paramtr spac is studid with minimal numbr of ncssary xprimnts only. [3, 4] Basd on th avrag output valu of th quality charactristic at ach paramtr lvl, main ffct analysis is prformd. Analysis of varianc (ANOVA) is thn usd to dtrmin which procss paramtr is statistically significant and th contribution of ach procss paramtr towards th output charactristic. With th main ffct and ANOVA analyss, possibl combination of optimum paramtrs can b prdictd. In an injction moulding procss dvlopmnt, OE can b applid in idntifying th machin procss paramtrs that hav significant influnc in th injction moulding procss output. Th asist way to do th st-up on th injction-moulding machin is basd on th machin st-up oprator or tchnician s xprinc, or trial and rror mthod. This trial and rror mthod is unaccptabl bcaus it is tim consuming and not cost ffctiv. Common quality problms or dfcts that com from an injction moulding procss includ voids, surfac blmish, shortshot, flash, jtting, flow marks, wld lins, burns, and war pag. Th dfcts of injction moulding procss usually aris from svral sourcs, which includ th pr-procssing tratmnt of th plastic rsin bfor th injction moulding procss, th slction of th injction-moulding machin, and th stting of th injction moulding procss paramtrs. [5] Th 1

2 objctiv of this papr is to obtain th optimal stting of machin procss paramtrs that will influnc Quality Charactristic (i.. Wight) and subsquntly, rduc th short shots. 2. Taguchi Tchniqu Taguchi and Konishi had dvlopd Taguchi tchniqus. [6] Ths tchniqus hav bn utilizd widly in nginring analysis to optimiz th prformanc charactristics within th combination of dsign paramtrs. Taguchi tchniqu is also powr tool for th dsign of high quality systms. It introducs an intgratd approach that is simpl and fficint to find th bst rang of dsigns for quality, prformanc, and computational cost. [7] In this study, paramtr dsign is coupld to achiv th optimum lvls of procss paramtrs lading to minimum Short Shots during th manufacturing of plastic parts Taguchi Paramtr sign Fallows chronological squnc as Slction Of Quality Charactristics Slction of Control Factors and Nois Factors Slction of Orthogonal Arrays Analysis of Rsults Confirmation of rsults 2.1. Slction of Quality Charactristic From th discussion with company popls strongly flt that wight of production part bars a dirct rlationship with occurrnc SHORT-SHOTS. Rcnt production parts masurmnt rvald that avrag wights of qualifid parts fll on th highr sid of th distribution whil thos with SHORT-SHOTS wr on th lowr nd so that wight of th part in grams is takn as quality charactristics. In Taguchi Mthod sirabl Prformanc is classifid in thr catgoris such as th-smallr-th-bttr, thlargr th-bttr, and th-nominal-th-bst. Signal to Nois analysis is dsignd to masur quality charactristic. It is givn by S/N = -10 log 10 (MS) (1) Whr MS= Man Squard ivision For th smallr th bttr charactristic, (3) Nominal th bst charactristic, MS = [(Y 1 m) 2 + (Y 1 m) 2 + (Y 1 m) 2 + )]/n (4) Whr Y 1, Y 2, Y 3 ar th rsponss and n is th numbr of tsts in a trial and m is th targt valu of th rsult. [8] Largr Wight valus rprsnt bttr or improvd minimum short shot. Thrfor, a Largr -th-bttr quality charactristic was implmntd and introducd in this study Slction of Control Factors In this study w hav considr 4 factors which affct majorly on quality charactristic such as (A) Injction, (B) Mold, (C) Mold, () Backprssur, (E). On of th advantags of Taguchi paramtr dsign is it can also considr uncontrollabl factors (Nois Factors) but in this study w hav considrd only controllabl factors Slction of Orthogonal Array Sinc 4 controllabl factors and thr lvls of ach factor wr considrd L9 Orthogonal Array was slctd for this study. 2.4 Analysis of Rsults Rsult Analysis was carrid out by making ANOVA to dtrmin prcntag ffct of ach paramtr on th quality charactristic. 3. Exprimntal Study 3.1 Injction Molding Procss Trials ar takn on Milacron 110 Injction Molding Machin by injcting Chlorinatd Poly Vinyl Chlorid (CPVC) matrial in 3/4 th inch T mold. Th spcimn is shown in Figur 1 Largr th bttr charactristic, (2) Figur 1. 3/4 th inch T 2

3 3.2. Exprimntal sign In ordr to dtrmin th optimal procss conditions and th ffct of th procssing paramtrs on th quality Charactristic i.. wight of CPCV 3/4 th inch TEE, th Taguchi mthod, xprimntal dsign was utilizd. Th controllabl factors slctd wr th (A) Injction, (B) Mold, (C) Mold, () Backprssur, (E. Tabl 1 givs th variabl factors and thir lvls. Four controllabl factors with thr lvls wr studid, as shown in Tabl 1; thrfor, th L9 orthogonal array (OA) was slctd for this study thrfor thr wr 9 trial conditions, four trials with ach trial condition was takn. Th signal- to-nois ratios (S/N) for ach xprimnt wr dtrmind by using largr is th bttr charactristic. Tabl 1. Controllabl factors and lvls Notation A B C Factor scription Injction Mold (mm/s) Mold Back 4. Rsult Analysis Lvl 1 Lvl 2 Lvl Trial Conditions and Rsults According to L9 layout thr ar nin trial conditions as shown in Tabl 2 Exp. Tabl 2. Layout for Exprimntal sign according to L9 Array A Injction B Mould ( mm/s) C Mould Back Four trials with ach trial condition wr takn thr S/N ratios with Largr is th bttr quality charactristic wr calculatd and summarizd in Tabl 3 Tabl 3 Summary Rport for iffrnt trials conductd during Exprimnts Trial 1 Wight In Grams S/N Ratios Grand Avrag Abov tst rsults wr studid and ffct of ach paramtr on S/N ratio was calculatd and plottd as shown in Figur 2 3

4 Man of SN ratios Intrnational Journal of Enginring Rsarch & Tchnology (IJERT) Main Effcts Plot for SN ratios ata Mans Injction 105 Mould 85 Signal-to-nois: Largr is bttr Mould spd 145 Back Figur 2 Effct of Procss paramtrs on S/N Ratio 4.2. Analysis of Varianc by QT-4 Softwar ANOVA was prformd by using a softwar qualitk-4 which givs significanc of ach factor in trms of Prcnt in th last column of th Tabl 4 Tabl 4. ANOVA Tabl Sr. Factor 1 Injction 2 Mold 3 Mold 4 Back Othr/ O F SS Varianc Pur Sum Error Total % From ANOVA it is clar that Mould closing spd and Back is th most significant factors. Th Optimum conditions and th optimum rsults ar calculatd with th hlp of ANOVA and givn in Tabl P Tabl 4.4 Optimum Condition and prformanc Sr. Factors 1 Injction 2 Mould 3 Mould 4 Back Lvl Lvl Contribution scription Total Contribution from All Factors Currnt Grand Avrag of Prformanc Expctd Rsult at Optimum Condition Conclusion Taguchi and ANOVA mthods wr usd to invstigat th ffcts of injction prssur, Mould, Mould and Back on th quality Charactristic ( i.. Wight) of 3/4 th inch T s. In Taguchi mthod, S/N ratios wr usd for dtrmining th optimal st of procss paramtrs. Th rsults showd that 125 Bar of Injction, 200mm/s of mould closing spd, 90 Bar of Mould and 15 Bar of Back gav maximum wight of sampls. ANOVA mthod gav th significanc dgr of th ach procss paramtr. According to th P-valus mor than 68, th Mould was ffctiv paramtr for Short Shots. 6. Rfrncs [1]Chn, R.S., L, H.H., and Yu, C.Y., Application of Taguchi s Mthod on th optimal procss dsign of an injction moldd PC/PBT automobil bumpr. Composit Structurs, 39, 1997, pp [2]Wang, W.H., and Tarng, Y.S., sign Optimization of cutting paramtrs for turning oprations basd on th Taguchi mthod. Journal of Matrials Procssing Tchnology, 84, 1998, pp [3]Phadk, M.S., Quality Enginring Using Robust sign. Prntic Hall Intrnational Inc., Nw York, [4] Roy, R.K., A primr on th Taguchi mthod. Comptitiv Manufacturing Sris, Van Nostrand Rinhold, Nw York [5]Shaik Mohamd Mohamd Yusoff, Jafri Mohd. Rohani, Wan Harun Wan Hamid & Edly Ramly A Plastic Injction Molding Procss Charactrisation Using Exprimntal sign Tchniqu: A Cas Study, Jurnal Tknologi, 41, 2004, pp 2 4

5 [6] Taguchi G, Konishi S. Taguchi mthods, orthogonal arrays and linar graphs, tools for quality Amrican supplir institut. Amrican Supplir Institut; 1987 [p. 8 35]. [7] Taguchi G. Introduction to quality nginring. Nw York: Mc Graw-Hill; [8] Roy, R. A Primr on th Taguchi Mthod. Nw York: Van Nostrand Rinhold;

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