• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Production of nylon 6 fr lever using an injection moulding tool and identification of optimum process parameter combination
 

Production of nylon 6 fr lever using an injection moulding tool and identification of optimum process parameter combination

on

  • 623 views

 

Statistics

Views

Total Views
623
Views on SlideShare
623
Embed Views
0

Actions

Likes
1
Downloads
6
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Production of nylon 6 fr lever using an injection moulding tool and identification of optimum process parameter combination Production of nylon 6 fr lever using an injection moulding tool and identification of optimum process parameter combination Document Transcript

    • INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME AND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online) IJMETVolume 3, Issue 3, September - December (2012), pp. 270-284© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2012): 3.8071 (Calculated by GISI) ©IAEMEwww.jifactor.com PRODUCTION OF NYLON-6 FR LEVER USING AN INJECTION MOULDING TOOL AND IDENTIFICATION OF OPTIMUM PROCESS PARAMETER COMBINATION S.Selvaraj1, Dr.P.Venkataramaiah2 1 Research Scholar, Department of Mechanical Engineering, Sri Venkateswara University College of Engineering and Senior Lecturer, Department of Tool & Die Making, Muruagapp Polytechnic College,Chennai 2 Associate Professor, Department of Mechanical Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India- 517502. ABSTRACT This research work on Optimization of Injection Moulding has been done in three phases. In the first phase, an Injection Moulding Tool is designed and fabricated for FR(Forward Reverse) lever, which is to control the direction of rotation of spindles for conventional machines. In the second phase, the influential parameters, called input parameters which affect the quality of FR lever are identified. The response parameters, called output parameters such as Shrinkage and Surface Roughness which are considered as quality characteristics of this product have also been identified. FR levers are produced using the fabricated injection moulding tool according to Taguchi L27 OA and response data are recorded. In the third phase, recorded experimental data are analyzed and optimum process parameters combination has been found by a combined method which is developed from the integration of the Principal Component Analysis (PCA) and Utility based Taguchi method. The obtained optimum parameters combination is conformed experimentally. Keywords: Injection Moulding, Principal component analysis (PCA), Shrinkage, Surface roughness, Utility based Taguchi method 1.0 INTRODUCTION Now a days, plastic products have more demand since they are of low cost, good corrosion resistant, light weight, flexible colours and have good life also. The costs of the plastic products are made less by production using various types of moulds. Many engineers and researchers have made research works on optimizing process parameters on Injecion 270
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEmoulding for various thermoplastic materials and attempt to reduce shrinkage and warpage.Some authors presented few case studies on improvement of Quality characteristic of surfaceroughness, shrinkage and warpage by applying Taguchi technique, Artificial NeuralNetwork(ANN), Genetic Algorithm(GA), Fuzzy logics and combination methods. Deng et al.applied Taguchi’s method and regression analysis to propose an approach for determining theoptimal process parameter settings in plastic injection molding under single qualitycharacteristic considerations [1]. Altan et al. minimized the shrinkage of rectangular- shapedspecimens by Taguchi experimental design and Neural network to predict the shrinkage ofthe part [2]. Hasan Kurtaran et al. proposed an efficient minimization method of warpage onthin shell plastic parts by integrating finite element (FE) analysis, statistical design ofexperiment method, response surface methodology(RSM), and genetic algorithm [3]. Shen etal. minimized the shrinkage of a plastic part by using the artificial neural network and geneticalgorithm [4]. Kurtaran et al. considered mold temperature, melt temperature, packingpressure, packing time and cooling time as the key process parameters during PIM and gotthe optimum values of process parameters in injection molding of a bus ceiling lamp base toachieve minimum warpage by using neural network model and genetic algorithm [5]. Factors that affect the quality of a molded part can be classified into four categories:part design, mold design, machine performance and processing conditions. The part and molddesign are assumed as established and fixed. During production, quality characteristics maydeviate due to variation in processing conditions caused by machine wear, environmentalchange or operator fatigue. Determining optimal process parameter settings criticallyinfluences productivity, quality, and cost of production in the plastic injection moulding(PIM) industry. Previously, production engineers used either trial-and-error method orTaguchi’s parameter design or ANN, Fuzzy method or combined method to determineoptimal process parameter settings for PIM[6-12]. However, these methods are unsuitable inpresent PIM because the increasing complexity of product design and the requirement ofmulti-response quality characteristics. A Principal Component Analysis(PCA) has been usedfor optimation of process parameters in different industrial application. Literature review reveals that there is a lack of research on design and fabrication ofinjection moulding tool and finding the optimal process parameters setting using PCA basedcombined approach. Hence, this paper focused on design, fabrication of Injection mould andproduction of Nylon-6 FR lever as well as the application of combined method which isdeveloped from the integration of the Principal Component Analysis (PCA) and Utility basedTaguchi method to determine the optimum parameter combination.2.0 PHASE I: DESIGN AND FABRICATION OF AN INJECTION MOULDINGTOOL FOR FR LEVER2.1 DESIGN OF AN INJECTION MOULDING TOOL FOR FR LEVER2.1.1 Modeling of FR lever and Injection moulding toolFirst, F-R lever model is modeled using ProE according to standard specifications. Two plateInjection moulding tool with taper parting surface is suitable for this kind of products andhence it is selected in the present work. It is decided to fabricate fully Automatic Injectionmoulding tool with ejectors assembly .Based upon the model of FR lever, the different partsof the injection moulding tool is identified and a model of injection moulding tool is createdin ProE 5 wildfire. The different parts of injection moulding tool with materials and size islisted in Table 1 271
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME2.1.2 Volume and Weight of FR LeverThe volume and weight of FR lever are found from created model as followsVolume of the component from model =23.750 ccDensity of the plastic material Used ( Nylon) =1.20g/cc (from standard databook)Weight of the component =volume * density = 23750*(1.20/10000) =28.5g2.1.3 Shot Capacity of MouldShot capacity of mould is the maximum amount of materials injected into the mould for oneshot.Shot capacity of mould= [total weight of the component]+[total weight of feed system]Weight of the feed system =10% of the component weight = (10/100)*28.5)=2.85g shot capacity= [total weight of the component× no. of cavities] + weight of the feed system = (28.5*1) +2.85 = 31.35g2.1.4 Selection of Injection Moulding Machine Based on shot capacity calculated above, the suitable injection moulding machine hasbeen selected. In the present study OPTIMA-75 of Electronica make is used for production ofFR lever.Specification of OPTIMA-75Clamping force : 75 tonsInjection pressure : 1486 barsShot weight : 123 gramsPump drive : 7.5kwMould thickness : 125– 310 mmDistance between the bars : 350 x 300mmMax. Day light : 610 mmScrew diameter : dia 35mm.2.1.5 Selection of Plastic MaterialNylon 6 has been selected for the F-R lever component because it have Very strong and rigid,Good abrasion resistant, heat resistant and dimensional accuracy, resistant to oils greases andcleaning fluids and high density. Fig.1 3D MODEL OF FR LEVER- CCOMPONENT DIAGRAM 272
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEFig 2 2D MODEL OF THE COMPONENT Fig.3. OPTIMA 75 INJECTION MOULDING WITH DIMENSIONS IN ‘mm’ MACHINE TABLE 1 BILL OF MATERIALS OF INJECTION MOULDING TOOL. S.NO MOULD ELEMENT MATERIAL SIZE IN ‘mm’ QTY 1 CAVITY PLATE EN 24 150X100X50 1 2 CORE PLATE EN 24 150X100X50 1 3. CORE BACK PLATE MS 150X100X15 1 4. EJECTOR PLATE MS 150X55X15 1 5. EJECTOR BACK PLATE MS 150X55X15 1 6. SPACER BLOCKS MS 150X50X10 2 7. BOTTOM SUPPORT PLATE MS 150X100X15 1 8. TOP PLATE MS 200X150X25 1 9. BOTTOM PLATE MS 200X150X25 1 10. CORE INSERT EN 36 φ 24X25 1 11. CORE SUB INSERT EN 36 φ 12X31 1 13. CAVITY INSERT EN 36 φ 11X41 1 14. SPRUE BUSH EN 36 φ 23X52 1 15. EJECTOR PINS STD φ6 4 16. ALLEN SCREW STD M6X25 4 17. ALLEN SCREW STD M8X85 4 18. ALLEN SCREW STD M10X30 4 273
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME2.2 FABRICATION OF INJECTION MOULDING TOOL FOR FR LEVERBased upon the design (shown in Table 1) of injection moulding tool, the following parts orelements are fabricated as follows:2.2.1 Making of Cavity plate and Core plateThe cavity and core plate provides the complete profile of the FR lever and taper partingsurface is used because of complicated profile of the FR lever. CNC program has beencreated from the profile drawing of FR lever and then the profile is made using VMC millingmachine. The runner is produced in the plate using EDM spark erosion machine, the ends arechamfered to avoid sharp corners and the profile is polished by diamond polish. 2.2.2 Making other Elements of Injection Moulding ToolCore Back Plate, Ejector Plate, Ejector Back Plate, Spacer Block, Bottom Plate, and BottomSupport Plate are prepared with help of shaping machine, grinding machine and the holes aremade and the counter bore for some plates are produced by position with DRO.2.2.3 Making of Core Sub Insert, Cavity Insert, Core Insert And Sprue BushCore sub insert, cavity insert, core insert and sprue bush are produced by lathe and surfacegrinding machine. Raw material is taken and the dimensions are checked, turning and facingoperation is done by using lathe machine to the required dimension. Grinding is done byusing surface grinding machine andends are chamfered.VMC milling machine is used producing special profile on core insertand the profile is polished by diamond polish. Vertical machining center (VMC) is acomputer numerical control machine used to fabricate any type of complicated jobs. Thismachine is used to produce core plate, cavity plate and top plate.After each component isfabricated and assembled to get an injection moulding tool by checking the all alignment forrequired mating parting as shown in Fig 6. Fig 4 Core back plate and other elements of the mould 274
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Fig 5 Vertical milling and VMC machine for Injection mould fabrication Fig 6 Cavity plate, core plate, top plate and Assembly of Injection moulding tool3.0 PHASE-II: PRODUCTION OF FR LEVER AND MEASUREMENT OFRESPONSES The fabricated injection mould tool is fitted in selected moulding machine andexperiments are conducted according to Taguchi L27 Orthogonal Array(OA) with 3 levelsand 10 input process parameters as shown in Table 2. Dimension of each specimen component have been measured using 3D CoordinateMeasuring Machine with a machine resolution of 0.05 micron at Accurate CalibrationService Laboratory which was certified by by National Accreditation Board for Testing andCalibration Laboratories(NABL). Based on the dimensions of the specimen, the Volume ofeach specimen has been calculated by creating a Model ProE 5.0 wildfire software.Percentage of Shrinkage of the each specimen has been calculated using the formula (୴୭୪୳୫ୣ ୭୤ ୲୦ୣ ୫୭୳୪ୢି୴୭୪୳୫ୣ ୭୤ ୲୦ୣ ୱ୮ୣୡ୧୫ୣ୬)%of shrinkage= ୴୭୪୳୫ୣ ୭୤ ୲୦ୣ ୫୭୳୪ୢ 275
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME The calculated value of percentage of shrinkage is recorded for each experiment as shown in Table 3. Surface roughness of each specimen is measured with portable stylus-type Talysurf (Mitutoyo make) as shown in Fig 7 and recorded in Table.3. Table 2 Process parameters and their levels in injection moulding machine of ER lever S.N Input parameters Symbol Level 1 Level 2 Level 3 (Controllable parameters) 1 Injection speed( mm/s , %) A 15 20 25 2 Injection pressure, (Bar) B 20 25 30 3 Holding pressure (Bar) C 15 20 25 4 Holding speed ( mm/s , %) D 15 20 25 5 Clamping pressure (Bar) E 30 40 50 6 Clamping speed ( mm/s , %) F 25 35 45 7 Injection time (Sec) G 1 1.5 2 8 Holding time (Sec) H 1.5 2 2.5 9 Cooling time (Sec) J 30 35 40 0 10 Nozzle temperature ( C) K 235 245 255 The other conditions are maintained as Refill speed is 80 mm/s, Refill pressure is 100 bar, Shot weight is 50 gram and Pre heat temp is 850 C . Fig 7 Measurement using CMM, Taylsurf and Injection moulding Tool with FR lever Table 3 Average Surface Roughness Characteristics and % of shrinkage valueExp. A B C D E F G H J K Surface Roughness ShrinkageRun Ra (µ) Ry (µ) Rq (µ) (%) 1 1 1 1 1 1 1 1 1 1 1 2.515 16.8225 3.26875 2.290960976 2 1 1 1 1 2 2 2 2 2 2 2.6425 16.62875 3.565 5.878247823 3 1 1 1 1 3 3 3 3 3 3 2.85875 16.79125 3.645 2.131741189 4 1 2 2 2 1 1 1 2 2 2 2.64 16.71125 3.47125 5.232735172 5 1 2 2 2 2 2 2 3 3 3 3.585 22.84375 4.70625 5.265334059 6 1 2 2 2 3 3 3 1 1 1 3.87375 22.91 5.0775 4.029811766 7 1 3 3 3 1 1 1 3 3 3 3.11125 22.31875 4.39875 5.746519484 8 1 3 3 3 2 2 2 1 1 1 3.35625 18.83 4.18625 6.77336339 9 1 3 3 3 3 3 3 2 2 2 3.17375 20.78625 4.13625 3.15421767 10 2 1 2 3 1 2 3 1 2 3 2.8775 16.89125 3.68875 7.372633732 11 2 1 2 3 2 3 1 2 3 1 3.94 25.10125 5.225 5.867920125 276
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME12 2 1 2 3 3 1 2 3 1 2 2.9525 19.255 3.9475 7.05899756113 2 2 3 1 1 2 3 2 3 1 3.22 17.41875 4.02 4.43811435614 2 2 3 1 2 3 1 3 1 2 3.0275 18.035 3.89625 9.025199215 2 2 3 1 3 1 2 1 2 3 3.2625 18.9925 4.2 8.16639024216 2 3 1 2 1 2 3 3 1 2 2.6575 16.67375 3.53125 4.0284655917 2 3 1 2 2 3 1 1 2 3 3.14875 20.37875 4.08375 7.51938800118 2 3 1 2 3 1 2 2 3 1 3.24625 15.7025 3.84 10.9386762319 3 1 3 2 1 3 2 1 3 2 2.18375 13.3575 2.93375 7.68069971520 3 1 3 2 2 1 3 2 1 3 2.85375 17.3525 3.85375 7.40500506921 3 1 3 2 3 2 1 3 2 1 2.84 18.19375 3.6775 6.72134437722 3 2 1 3 1 3 2 2 1 3 2.17125 13.8325 3.0275 5.75442444623 3 2 1 3 2 1 3 3 2 1 2.8125 15.6575 3.625 2.46488353424 3 2 1 3 3 2 1 1 3 2 2.49 16.29375 3.2275 6.3613454325 3 3 2 1 1 3 2 3 2 1 2.4275 14.6275 3.19875 5.01222611226 3 3 2 1 2 1 3 1 3 2 2.26625 13.6825 3.08375 6.14992497427 3 3 2 1 3 2 1 2 1 3 2.49625 16.82875 3.3125 5.441488134 4.0 PHASES-III: IDENTIFICATION OF OPTIMUM PARAMETERS USING A COMBINED APPROACH The recorded responses data are analysed and optimum analysis of experimental data using combined approach of Principle Components Analysis and utility based taguchi method. The experimental data(Table 3) are analyzed using Combined Approach to identify the optimum process parameters setting as follows Step 1: Normalization of the responses (quality characteristics) When the range of the series is too large or the optimal value of a quality characteristic is too enormous, it will cause the influence of some factors to be ignored. The original experimental data must be normalized to eliminate such effect. There are three different types of data normalization according to the requirement LB (Lower-the-Better),HB (Higher-the-Better) and NB (Nominal-the-Best). The normalization is taken by the following equations. (a) LB (Lower-the-Better) min X i (k ) X * (k ) = X (k ) ----(1) (b) HB (Higher-the-Better) X i (k ) X * (k ) = max X i (k ) ----(2) (c) NB (Nominal-the-Best) min{X i (k ), X 0b (k )} X * (k ) = max{X i (k ), X 0b (k )} ----(3) Here, i = 1, 2, ........, m; k = 1, 2, ........., n X * (k ) is the normalized data of the k th element in the i th sequence. X 0 (k ) is the desired value of the k th quality characteristic. After data normalization ,the Value of X*(K) will be between 0-1.The series X*i i=1,2,3…m ,can be viewed as a comparative sequence used in the present case. For present study LB is applicable because there is a need to minimize the responses (surface roughness, shrinkage) 277
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEStep 2: Checking for correlation between two quality characteristicsLet,Qi = {X 0 (i), X1 (i), X 2 (i), ............, X m (i)}where, i = 1, 2, ......., nIt is the normalized series of the ith quality characteristic. The correlation coefficient betweentwo quality characteristics is calculated by the following equation: Cov(Q j , Q k )ρ= -----(4) σ Q j × σ QK j =1, 2, 3......, n. here, k = 1, 2, 3, ........, n., j ≠kHere, ρjk is the correlation coefficient between quality characteristic j and qualitycharacteristic k ; Cov (Q j , Qk ) is the covariance of quality characteristic j and qualitycharacteristic k ; σ and σ are the standard deviation of quality characteristic j and kquality characteristic k , respectively.The correlation is checked by testing the following hypothesis.H0: ρ jk = 0 (There is no correlation)H1: ρ jk ≠ 0 (There is correlation) -----(5)Step 3: Calculation of the principal component score(a) Calculate the Eigen value λk and the corresponding eigenvectorβk (k = 1, 2, ......, n) from the correlation matrix formed by all quality characteristics.(b) Calculate the principal component scores of the normalized reference sequenceand comparative sequences using the equation shown below: nYi (k ) = ∑Xi∗ (j)βkj, i = 0,1,2,.......,m; k =1, 2,........ n. , ---(6) J =1Here, Yi (k ) is the principal component score of the k th element in the ith series.X * ( j) is the normalized value of the j th element in the i th sequence, and β kj is the j thelement of eigenvector β kStep 4: Estimation of quality loss ∆0,i (k )∆0,i (k ) is the absolute value of difference between X 0 (k ) and X i (k ) differencebetween desired value and ith experimental value for kth response. If responses arecorrelated then instead of using X 0 (k ) and X i (k ) , Y0 (k ) and Yi (k ) should be used.  X 0 ∗ X i (k ) − X i ∗ (k)  no significant correlation between quality characteristics∆0,i (k )=  -----(7)  Y 0 (k ) − Y i (k)  significant correlation between quality characteristicsStep 5: Adaptation of utility theory: Calculation of overall utility indexAccording to the utility theory, if X i is the measure of effectiveness of an attribute (or qualitycharacteristics) i and there are n attributes evaluating the outcome space, then the joint utilityfunction can be expressed as:U ( X1 , X 2 ,......... ........,X n ) = f (U1 ( X1 ).U2 ( X 2 )......... .........Un ( X n ))Here Ui ( X i ) is the utility of the ith attribute. 278
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEThe overall utility function is the sum of individual utilities if the attributes areindependent, and is given as follows: nU ( X1, X 2 , ................., X n ) = ∑ Ui ( X i ) − − (8) i=1The attributes may be assigned weights depending upon the relative importance orpriorities of the characteristics. The overall utility function after assigning weights to theattributes can be expressed as: nU ( X1, X 2 ,................., X n ) = ∑ Wi .Ui ( X i ) i =1Here, Wi is the weight assigned to the attribute i . The sum of the weights for all theattributes must be equal to 1.A preference scale for each quality characteristic is constructed for determining its utilityvalue. Two arbitrary numerical values (preference number) 0 and 9 are assigned to the justacceptable and the best value of quality characteristic respectively. The preference numberPi can be expressed on a logarithmic scale as follows:  Xi Pi = A × log   Xi  -----(9)Here, X i is the value of any quality characteristic or attribute i,Xi is just acceptable value ofquality characteristic or attribute i and A is a constant. The value A can be found by thecondition that if Xi = X * (where X * is the optimal or best value), then Pi = 9 .Therefore, 9 A= X∗ log Xi ----(10)The overall utility can be expressed as follows: nU = ∑WiPi i −1 ---(11)Subject to the condition: n∑Wi = 1i =1Among various quality characteristics types, viz. Lower-the-Better, Higher-the-Better, andNominal-the-Best suggested by Taguchi, the utility function would be Higher-the- Bettertype. Therefore, if the quality function is maximized, the quality characteristics consideredfor its evaluation will automatically be optimized (maximized or minimized as the case maybe).In the proposed approach based on quality loss (of principal components) utility valuesare calculated. Utility values of individual principal components are accumulated tocalculate overall utility index. Overall utility index servers as the single objective functionfor optimization.Step 6: Optimization of overall utility index using Taguchi methodFinally overall utility index is optimized (maximized) using Taguchi method. Forcalculating S/N ratio, HB criterion is selected. 279
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME5.0 RESULTS AND DISCUSSIONExperimental data with L27 OA are noted and listed in Table 3. For all surface roughnessparameters and % of shrinkage, LB criterion has been selected. Normalized experimentaldata are shown in Table 4. The correlation coefficients between individual responses havebeen computed using Equation 4. Table 5 represents Pearson’s correlation coefficients. It hasbeen observed that all the responses are correlated (coefficient of correlation having non-zerovalue). Table 5 presents Eigen values, eigenvectors, accountability proportion (AP) andcumulative accountability proportion (CAP) computed for the four major qualityindicators (ψ ) . It has been found that the four principal components, ψ1 ,ψ 2 ,ψ 3, ψ 4 cantake care of 71.48%, 0.3%, 2.93% and 25.29% variability in data respectively. Table 4 Normalized values of Surface roughness and % of shrinkage Exp. % of Ra Ry Rz No shrinkage 1 0.638325 0.670186 0.625598 0.209437 2 0.670685 0.662467 0.682297 0.537382 3 0.725571 0.668941 0.697608 0.194881 4 0.670051 0.665754 0.664354 0.47837 5 0.909898 0.910064 0.900718 0.48135 6 0.983185 0.912704 0.97177 0.3684 7 0.789657 0.889149 0.841866 0.52534 8 0.85184 0.750162 0.801196 0.619212 9 0.80552 0.828096 0.791627 0.288355 10 0.73033 0.672925 0.705981 0.673997 11 1 1 1 0.536438 12 0.749365 0.767093 0.755502 0.645325 13 0.817259 0.69394 0.769378 0.405727 14 0.768401 0.71849 0.745694 0.825072 15 0.828046 0.756636 0.803828 0.746561 16 0.674492 0.66426 0.675837 0.368277 17 0.799175 0.811862 0.781579 0.687413 18 0.823921 0.625566 0.734928 1 19 0.554251 0.532145 0.561483 0.70216 20 0.724302 0.6913 0.73756 0.676956 21 0.720812 0.724815 0.703828 0.614457 22 0.551079 0.551068 0.579426 0.526062 23 0.713832 0.623774 0.69378 0.225337 24 0.63198 0.649121 0.617703 0.581546 25 0.616117 0.58274 0.612201 0.458211 26 0.57519 0.545092 0.590191 0.562218 27 0.633566 0.670435 0.633971 0.497454 280
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 5 Eigen values, Eigen vectors and Accountability proportion Eigen values 2.8594 0.0119 0.1172 1.0115 V =Eigen vectors -0.5757 -0.5371 -0.6127 0.0685 -0.5677 -0.2800 -0.0903 -0.0903 -0.5884 0.7957 -0.1433 0.0118 -0.0049 0.0021 0.1138 0.9935 Accountability Proportion (AP) Ap1 Ap2 Ap3 Ap4 0.7148 0.003 0.0293 0.2529 Cumulative Accountability Proportion (CAP) cap1 cap2 cap3 cap4 0.7148 0.7178 0.7471 1 Table 6: Major Principal Components and Quality loss estimates for principal components Major Principal Components Quality loss estimatesExp. No P1 P2 P3 P4 QL1 QL2 QL3 QL4 Ideal -1.7368 -0.0193 0.1267 0.9834 - - - - sequence 1 -1.1171 -0.0323 0.0584 0.1986 0.6197 -0.013 -0.0683 -0.7848 2 -1.1663 -0.0017 0.0618 0.528 0.5704 0.0176 -0.0649 -0.4554 3 -1.2089 -0.0215 -0.008 0.1911 0.5278 -0.0022 -0.1347 -0.7923 4 -1.157 -0.0167 0.0606 0.4688 0.5798 0.0026 -0.0661 -0.5146 5 -1.5729 -0.0258 0.068 0.4689 0.1639 -0.0065 -0.0587 -0.5145 6 -1.6578 -0.0096 0.0021 0.3623 0.079 0.0097 -0.1247 -0.6211 7 -1.4574 -0.0021 0.139 0.5056 0.2794 0.0172 0.0123 -0.4778 8 -1.3908 -0.0288 0.0106 0.6152 0.346 -0.0095 -0.1162 -0.3682 9 -1.4011 -0.034 0.0626 0.2762 0.3357 -0.0147 -0.0641 -0.7073 10 -1.2212 -0.0175 0.0455 0.6672 0.5156 0.0018 -0.0812 -0.3163 11 -1.7345 -0.0203 0.074 0.5229 0.0023 -0.001 -0.0528 -0.4605 12 -1.3146 -0.0148 0.0959 0.6321 0.4221 0.0045 -0.0308 -0.3514 13 -1.3192 -0.0202 -0.0312 0.4054 0.4176 -0.0009 -0.1579 -0.578 14 -1.2931 -0.0188 0.0687 0.8162 0.4437 0.0005 -0.058 -0.1672 15 -1.3829 -0.0154 0.0442 0.7395 0.3538 0.0039 -0.0825 -0.2439 16 -1.1649 -0.0097 0.0426 0.36 0.5719 0.0096 -0.0842 -0.6234 17 -1.3843 -0.0332 0.1008 0.6736 0.3525 -0.0139 -0.0259 -0.3099 18 -1.2669 -0.0308 -0.0153 1.0021 0.4699 -0.0115 -0.142 0.0186 19 -0.9551 0.0016 0.069 0.6941 0.7817 0.0209 -0.0577 -0.2893 20 -1.2468 0.0057 0.0591 0.6684 0.49 0.025 -0.0676 -0.315 21 -1.2436 -0.0288 0.0847 0.6026 0.4931 -0.0095 -0.042 -0.3808 22 -0.9737 0.0119 0.0629 0.5174 0.7631 0.0312 -0.0638 -0.466 23 -1.1744 -0.0055 -0.0315 0.2246 0.5624 0.0138 -0.1582 -0.7588 24 -1.0987 -0.0285 0.0896 0.5697 0.6381 -0.0092 -0.0371 -0.4137 25 -1.048 -0.006 0.035 0.452 0.6888 0.0133 -0.0917 -0.5314 26 -0.9907 0.0092 0.0461 0.5557 0.7461 0.0285 -0.0806 -0.4278 27 -1.1209 -0.0225 0.0931 0.4845 0.6159 -0.0032 -0.0336 -0.4989 281
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 7 Utility values related Individual principal components and Overall utility index and S/N values Exp. Overall U1 U2 U3 U4 S/N No. utility 1 0.3568 1.9108 4.1761 0 1.6109 4.1414 2 0.4839 1.2448 4.4327 1.3088 1.8676 5.4254 3 0.6031 5.7564 0.7997 -0.023 1.784 5.0281 4 0.459 5.3827 4.3393 1.015 2.799 8.9401 5 2.3994 3.4099 4.927 1.0155 2.938 9.3609 6 3.521 2.5484 1.1854 0.5627 1.9544 5.8202 7 1.5801 1.2982 12.7112 1.1933 4.1957 12.4561 8 1.2519 2.5983 1.5359 1.8198 1.8015 5.1126 9 1.2984 1.6366 4.4892 0.2502 1.9186 5.6597 10 0.6393 6.2312 3.3151 2.1857 3.0928 9.8071 11 8.9583 7.54 5.4619 1.2819 5.8105 15.2843 12 0.9464 4.1981 8.1303 1.9326 3.8018 11.5999 13 0.963 7.6904 0.008 0.7356 2.3492 7.4186 14 0.87 9.021 4.9887 3.7183 4.6495 13.3481 15 1.2175 4.5444 3.2381 2.8106 2.9526 9.4042 16 0.4801 2.5729 3.1392 0.5537 1.6865 4.5395 17 1.2233 1.7583 9.0018 2.2347 3.5545 11.0156 18 0.7817 2.1721 0.5362 8.9959 3.1215 9.8872 19 -0.0001 0.8768 5.0174 2.3997 2.0735 6.3339 20 0.7174 0.4815 4.2288 2.1951 1.9057 5.6011 21 0.7076 2.5956 6.5992 1.7392 2.9104 9.2791 22 0.0369 0.0022 4.5161 1.2535 1.4522 3.2404 23 0.5059 1.7838 -0.0008 0.0809 0.5924 -4.5473 24 0.3118 2.6676 7.2061 1.5396 2.9313 9.3411 25 0.1944 1.8558 2.7113 0.9376 1.4248 3.0749 26 0.0715 0.1945 3.354 1.4594 1.2699 2.0751 27 0.3661 4.9475 7.7004 1.0894 3.5259 10.9453Major principal components is obtained using Equation 6. These have been furnished inTable 6. Quality loss estimates (difference between ideal and actual gain) for aforesaidmajor principal components have been calculated (Equation7) and also presented in Table 6.Based on quality loss, utility values corresponding to the four principal components havebeen computed using Equations 9, 10.In all the cases minimum observed value of the quality loss (from Table 6) has beenconsidered as its optimal value or most expected value; whereas maximum observed valuefor the quality loss has been treated as the just acceptable value. Individual utility measurescorresponding to four major principal components have been furnished in Table 7. Theoverall utility index has been computed using Equation 11 and tabulated in Table 7 with theircorresponding (Signal-to-Noise) S/N ratio. In this computation it has been assumed that allquality indices are equally important (same priority weight age, 25%). Figure 8 reflects S/Nratio plot for overall utility index; S/N ratio being computed using equation (12). 282
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME 1 t 1 SN ( Higher − the − better) = −10 log ∑ 2  ---(12)  t i =1 yi Here t is the number of measurements, and yi the measured ith characteristic value i.e. ithquality indicator. Optimal parameter setting has been evaluated from Figure. The optimalsetting should confirm highest utility index (HB criterion). Fig 8 S/N ratios for predicated optimal setting The predicted optimal setting is A2 B1 C2 D2 E3 F2 G1 H2 J3 K36.0 CONCLUSIONSCombined approach of PCA and Utility based Taguchi method is successfully applied in thepresent study and the following conclusions are drawn from the results of the experimentsand analysis of the experimental data in connection with correlated multi- responseoptimization in injection moulding of FR lever. • Based on the analysis and results, it is concluded that PCA is most powerful tool to eliminate response correlation by converting the correlated responses into uncorrelated quality indices, called principal components which have been treated as response variables for optimization. • Based on the PCA method, it has been found that first principal component ψ1 and fourth principal component ψ 4 can take care of 71.48% and 25.29% variability in data respectively, which shows that Surface roughness Ra and % of shrinkage are the most influence quality characteristics. • Utility based Taguchi method has been found fruitful for evaluating the optimum parameter setting for these kind of multi-objective optimization problems. • The proposed algorithm greatly simplifies the optimization of injection moulding parameters with multiple performance characteristics. Thus, the solutions from this method can be a useful reference for injection mould makers and related industry. 283
    • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEREFERENCES [1] Deng WJ, Chen CT, Sun CH, Chen WC, Chen CP. An effective approach for process parameteroptimization in injection molding of plastic housing components. Polym-Plast Technol Eng2008;47:910–9. [2] Altan M. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural networkmethods. Mater Des 2010;31:599–604.[3] Kurtaran H, Erzurumlu T. Efficient warpage optimization of thin shell plastic parts using responsesurface methodology and genetic algorithm. Int J Adv Manuf Technol 2006;27: 468–72.[4] Shen CY, Wang LX, Li Q. Optimization of injection molding process parameters usingcombination of artificial neural network and genetic algorithm method. J Mater Process Technol2007;183:412–8.[5] Kurtaran H, Ozcelik B, Erzurumlu T. Warpage optimization of a bus ceiling lamp base usingneural network model and genetic algorithm. J Mater Process Technol 2005;169:314–9. [6]Chen, R.S., Lee, H.H., Yu, C.Y., 1997. Application of Taguchi’s method on the optimal processdesign of an injection molded PC/PBT automobile bumper. Compos. Struct. 39, 209–214. [7] Ozcelik B, Sonat I. Warpage and structural analysis of thin shell plastic in the plastic injectionmolding. Mater Des 2009;30:367–75.[8] Huang MC, Tai CC. The effective factors in the warpage problem of an injection molded part witha thin shell feature. J Mater Process Technol 2001;110:1–9.[9] B.H.M. Sadeghi,ABP-neural network predictor model for plastic injection molding process, J.Mater. Process. Technol. 103 (3) (2000) 411–416. [10] S.L.B. Woll, D.J. Cooper, Pattern-based closed-loop quality control for the injection moldingprocess, Polym. Eng. Sci. 37 (5) (1997) 801– 812. [11] H. Kurtaran, B. Ozcelik, T. Erzurumlu, Warpage optimization of a bus ceiling lamp base usingneural network model and genetic algorithm, J. Mater. Process. Technol. 169 (2005) 14–319.[12] B. Ozcelik, T. Erzurumlu, Comparison of the warpage optimization in the plastic injectionmolding, using ANOVA, neural network model and genetic algorithm, J. Mater. Process. Technol.171 (2006) 437–445.[13] Antony J., (2000), “Multi-response optimization in industrial experiments using Taguchi’squality loss function and Principal Component Analysis”, Quality and Reliability EngineeringInternational, Volume 16, pp.3-8.[14]. Datta S., Nandi G., Bandyopadhyay A. and Pal P. K., (2009), “Application of PCA based hybridTaguchi method for multi-criteria optimization of submerged arc weld: A case study”, ForInternational Journal of Advanced Manufacturing Technology. (Article In press) DOI10.1007/s00170-009-1976-0.[15] Baesso, R., Lucchetta, G., 2007. Filling balance optimization by best gate location. In: SPEANTEC Proceedings, pp. 662–666.[16]Cao, W., Shen, C., 2005. Two solutions for three-dimensional flow simulation of injectionmolding. In: SPE ANTEC Proceedings, pp. 486–490.[17]Chang, T.C., 2001. Shrinkage behaviour and optimization of injection molded parts studied by theTaguchi method. Polym. Eng. Sci. 41, 703–710.[18] Chang, R.Y., Chang, Y.R., Peng, Y.H., Yang, W.H., Hsu, C.H., 2007a. True 3D simulation offlow-induced residual stress in injection molding. In: SPE ANTEC Proceedings, pp. 2452–2455.[19] Chang, Y.R., Chiu, H.S., Yang, W.H., Chang, R.Y., 2007b. A novel approach for predictingbirefringence of optical parts. In: SPE ANTEC Proceedings, pp. 2490–2493.[20] Department of Mechanical Engg., Gehze Institute of Technology, Turkey, Optimization ofinjection parameters for mechanical properties of specimens with weld line of polypropylene usingTaguchi method, Int. Communications in Heat and Mass transfer 38(2011),1067-1072 284