Docstoc

30120130404044

Document Sample
30120130404044 Powered By Docstoc
					 International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
  INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
                            AND TECHNOLOGY (IJMET)

 ISSN 0976 – 6340 (Print)
 ISSN 0976 – 6359 (Online)                                                     IJMET
 Volume 4, Issue 4, July - August (2013), pp. 401-413
 © IAEME: www.iaeme.com/ijmet.asp
 Journal Impact Factor (2013): 5.7731 (Calculated by GISI)                ©IAEME
 www.jifactor.com




     IMPROVEMENT OF TOUGHNESS AND STIFFNESS OF BIOPOLYMER
           BLENDS USING PCA BASED TAGUCHI APPROACH

                  R.Umamaheswarrao1, T.VenkataSylaja2, Dr. K N S Suman3
           1
             (Associate professor, Department of Mechanical Engineering, GMRIT, INDIA)
                2
                  (PG Student Department of Mechanical Engineering, GMRIT, INDIA)
         3
           (Assistant Professor Department of Mechanical Engineering, A U, Visakhapatnam)


 ABSTRACT

            Biodegradable polymeric blends were widely used in the present days and the focus was
 made towards them. To make them more useful for wider applications among the human kind, the
 present study has been made to increase their mechanical properties toughness and stiffness. In order
 to achieve the improved properties, PCA based Taguchi technique has been selected and its
 methodology was implemented .To prepare the blend melt blending technique has been implemented
 and to obtain the specimen compression molding process was used by assuming five process
 parameters like temperature, pressure, soak time, cooling rate and composition of the blend. In this
 PCA method multiple objectives of the optimization problem were converted into a single objective
 function known as the principal component. After finding out the principal component the S/N ratios
 are plotted and the optimum parameter settings were tabulated.

 Keywords: Biodegradable polymeric blends, toughness and stiffness.

I.      INTRODUCTION

         Plastics play a significant role in the environmental, societal and economic dimensions of
 sustainable development. But due to their origin from petroleum based products which were
 disintegrating and due to their adverse effects on environment, there was a growing need for an
 alternative. Biopolymers were the best alternative since they easily get degraded and they were
 originated from plants, which restricts our utilization of petroleum products. Of the many bio-based
 and biodegradable polymers, poly-lactic acid (PLA) has been attracting much attention due to its
 mechanical properties resembling that of present day commodity plastics such as PE, PP and PS. It
 can be processed using injection-molding, compression-molding, extrusion, thermoforming etc. PLA
 has high modulus, reasonable strength, excellent flavor and aroma barrier capability, good heat seal

                                                 401
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

ability and can be readily fabricated, thereby making it one of the most promising biopolymers for
varied applications. Despite these desirable features, several drawbacks tend to limit its widespread
applicability such as high cost, brittleness, and narrow processing windows. Polymer blending was a
method for obtaining properties that the individual do not possess. Biodegradable polymer such as
with poly (butylenessuccinatedadipate) (PBSA) (A Bhatia, R Gupta 2007), poly (butylene adipate-
co-terephthalate) (PBAT) (JT Yeh, CH Tsou, CY Huang, 2010), poly (e caprolactone) (PCL) (Todo
et al., 2007) and poly (ethylene succinate) (PES) (Lu, Qiu, and Yang, 2007), etc are among the better
alternatives for blending with PLA.
         Apart from the above mentioned polymers Poly (€ - caprolactone) (PCL) was another
polymer which seems to be promising due to its encouraging properties and its compatibility with
many types of polymers (Hung and Edelman, 1995). To prepare the blends of these polymers Melt
blending setup is used, and molded into a sheet of ASTM standards to carry out the experiment. The
experiments were carried out according to the Taguchi orthogonal array by taking the process
parameters as Temperature, pressure, soak time, cooling rate and composition. After obtaining the
Toughness and Stiffness values PCA method was applied to obtain the Signal to noise ratios,
ANOVA is calculated, optimum values were tabulated.

II. PCA BASED TAGUCHI METHOD

1. Getting experimental data
         The experimental values for the four output responses are tabulated and are taken
    to optimization.
2. Normalization of experimental data
             As the desired optimal setting is for higher Tensile Strength, Elongation, Flexural
     Strength and Impact Strength, the experimental data is normalized by using the higher-the-
     better (HB) criterion.

           Higher-the-better (HB) criterion, the normalized data can be expressed as:

                                                        yi (k ) − min yi (k )
                                          xi (k ) =
                                                      max yi (k ) − min yi (k )

                  Here xi(k) is the value after the grey relational generation, min yi (k) is the smallest
   value of yi (k) for the kth response, and max yi(k) is the largest value of yi(k) for the kth response.

3. Calculation of Variance-Covariance matrix

         3.1   Calculating the mean of X using the following formula:




               Similarly calculate Mean Y, Mean Z, Mean W.

         3.2   The formulas used for variance and covariance are:




                                                       402
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME




              Then calculate x= (xi – Mean X), y= (yi – Mean Y).
                    Then calculate x2, y2, xy.

        3.3   Variance-covariance matrix for the four variables will be

                                       Cov(x, x) cov(x, y)
                                       Cov(x, x) cov(x, y)

4.   Finding Eigen values and Eigen vectors of the variance-covariance matrix.
5.   Calculation of Accountability proportion and Cumulative Accountability proportion.
6.   Calculation of individual principal components and composite principal components.

                              GETTING EXPERIMENTAL DATA

                            NORMALIZATION OF EXPERIMETAL

                       CALCULATION OF VARIANCE COVARIANCE

                         FIND THE EIGEN VALUE ANFD EIGEN
                       VECTORS OF VARIANCE AND COVARIANCE
                                      MATRIX

                             CALCULATING ACCOUNTABILITY
                                 PROPORTION AP &CAP

                        CALCULATION OF INDIVIDUAL PRINCIPAL
                                 COMPONENTS (Ψi)

                        CALCULATION OF COMPOSITE PRINCIPAL
                                   COMPONENT



                                CALCULATION OF S/N RATIO

                                               ANOVA

                        PLOT FOR OPTIMAL PARAMETER SETTING
                                     FOR CPC(Ψi)

                 Figure – 2.1: Schematic representation of PCA based approach.

                                                403
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

III. EXPERIMENTATION

A. Materials used

1) POLYLACTIC ACID(PLA)

                                                          Table 3.1 Properties of PLA

                                               Tensile modulus            2.7-16            Gpa

                                                Melting index              8/10             g/min

                                                     Density          1.21-1.43             g/cm3

                                                 Crystalinity              37%               _
    Fig 3.1 Poly Lactic Acid

2) POLYCAPROLACTANE(PCL)


                                                         Table3.2 Properties of PCL


                                             Melting index          7/10              g/min

                                                Density          1.02-1.12            g/cm3

                                                                                        o
                                              Melting point          60                 C

   Fig3.2 PolyCaproLactane (PCL)


3) Blend preparation
        The pellets of both PLA and PCL were initially dried in vacuum oven at a temperature of
50oC for 2 days to remove water before processing through the Rheomix shown in Fig.3.3. Drying is
necessary to minimize the hydrolytic degradation of the polymers during melt processing in the
HakeeRheomix. Blends of PLA and PCL with 90/10, 80/20, 70/30 were extruded by melt blending at
170oC (zone-5).Measured quantities of each polymer were first mixed in a container before blending
in aHakeeRheomix.The Rheomix was operated at 170oC,160oC,150oC, 140oC and130oC at zones 5,
4, 3, 2 and 1 respectively and 60 rpm screw speed for compounding of all the blends. After
compounding the blend was extruded through an orifice of 1mm diameter and pelletized using a
pelletize as shown in Fig.3.4. All the blends were given the same processing treatment to maintain
the overall consistency. Prepared blends were again dried at 50oC in vacuum oven for 12 hours
before compression.



                                               404
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

B) Experimental design

1) In order to determine optimum process parameters and effect of process control parameters
Taguchi orthogonal array was selected. The controllable parameters are taken as Pressure (P),
Temperature (T), Soak time(S), Cooling rate (CR), Composition(C). Five controllable parameters
with four levels were studied as shown in Table – 3.3

                                Table 3.3: Process control parameters

     Process
                     Notation         Units      Level 1         Level 2   Level 3    Level 4
    Parameters
                                       0
   Temperature            T                C          170         175           180    185
     Pressure             Pr          M Pa            2.5          5            7.5     10
    Soak Time             ST          Min              0           10           20      30
  Cooling System          CS           ---        Natural        Forced     Water       --

2) Taguchi L16 OA design was used for Experimentation. As mentioned in table 3.4.

                                 Table 3.4: Taguchi L16 OA design

                         S.NO         T          P          ST      CS     C

                          1          170        2.5         5       N      0
                          2          170         5          10      F      10
                          3          170        7.5         15      W      20
                          4          170        10          20      F      30
                          5          175        2.5         10      W      30
                          6          175         5          5       F      20
                          7          175        7.5         20      N      10
                          8          175        10          15      F      0
                          9          180        2.5         15      F      10
                          10         180         5          20      W      0
                          11         180        7.5         5       F      30
                          12         180        10          10      N      20
                          13         185        2.5         20      F      20
                          14         185         5          15      N      30
                          15         185        7.5         10      F      0
                          16         185        10          5       W      10

                                                405
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

3) Specimen preparation as per DOE


      Specimen of 25mmx6mmx4mm are prepared by compression molding at 180oC and 13MPa




                                      Fig3.3 Hot Press

                          MODEL: MPE-15-300 TONS. Air cooling




                             Fig 3.4 Compression molding plates




                            Fig 3.5: Compression molded specimen


                                            406
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

Pellets of PLA/PCL blends were kept in a flash picture frame mould( as shown fig 3.4) and placed
between the hot plates of hydraulic press(as shown fig 3.3). The assembly is heated and compressed
for the measured amount of time. Then, the polymer is cooled to room temperature at a specified
cooling rate under constant pressure. Then the hot pressed sheet is removed from the flash picture
frame mould and conditioned at 25oC of for 24 hours .The specimens were cut as per ASTM
standered using wire hacksaw.the specimen as shown in fig 3.5.

4) Toughness and Stiffness measurement.
       The compression molded specimen is carried out to characterization in an INSTRON -3382
model UTM which is equipped with 100KN load cell, gauge length of 50mm and crosshead speed of
5 mm/min. Tensile testing was carried out according to the ASTM D 638-08 (Type- I), a standard
test method for determining tensile properties of plastics. The area under Stress-Strain curve
evaluates to Toughness and slope to Stiffness.and the resultant values of all tests were tabulated in
table 3.5.




                                 Fig3.6: Universal testing machine




                                                407
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

                     Table 3.5: Taguchi L16 OA design Experimental results

                                                             Experimental results
                 T        P      ST     CS         C
     S.NO                                              Toughness(J/m2) Stiffness(N/m2)

        1       170       2.5     5      N         0        98.46            2.32

        2       170        5     10      F      10         121.16            1.83

        3       170       7.5    15      W      20         235.34            1.87

        4       170       10     20      F      30          72.21            0.67

        5       175       2.5    10      W      30         253.85            1.85

        6       175        5      5      F      20          229.4             2.3

        7       175       7.5    20      N      10         184.95             2.6


        8       175       10     15      F         0        150.5             2.9


        9       180       2.5    15      F      10         169.97            2.51


       10       180        5     20      W         0       169.05            2.31

       11       180       7.5     5      F      30         194.58            1.67

       12       180       10     10      N      20          194.9            2.38

       13       185       2.5    20      F      20         176.22            1.97

       14       185        5     15      N      30          87.65            1.73


       15       185       7.5    10      F         0       129.51            2.28


       16       185       10      5      W      10         189.55            2.87




                                             408
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

IV. RESULTS AND DISCUSSIONS

       Results obtained through Experimentation (table3.4) were normalized and the resulting
values are tabulated in table 4.1.

                                 Table 4.1 normalized data
                                                        NORMALIZED DATA
           S.NO      T      P     ST    CS     C
                                                   Toughness(J/m2)   Stiffness(N/m2)

             1        1      1     1     1     1        0.1445           0.7399


             2        1      2     2     2     2        0.2694           0.5201


             3        1      3     3     3     3        0.8980           0.5381


             4        1      4     4     2     4             0             0


             5        2      1     2     3     4             1           0.5291


             6        2      2     1     2     3        0.8653           0.7309


             7        2      3     4     1     2        0.6206           0.8654


             8        2      4     3     2     1        0.4310             1


             9        3      1     3     2     2        0.5382           0.8251


            10        3      2     4     3     1        0.5331           0.7354


            11        3      3     1     2     4        0.6376           0.4484


            12        3      4     2     1     3        0.6754           0.7668


            13        4      1     4     2     3        0.5726           0.5515


            14        4      2     3     1     4        0.0850           0.4753


            15        4      3     2     2     1        0.3154           0.7219


            16        4      4     1     3     2        0.6460           0.9865




                                             409
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

Normalized data (table4.1) converted into principal components. and the resulting values are
tabulated in table4.2.

                                    Table 4.2: Principal components
               Trail no                 ψ 1 (1st P.C)               ψ 2 (2nd P.C)
                  1                        -0.5954                     0.8844
                  2                        -0.2507                     0.7895
                  3                         0.3599                     1.4361
                  4                            0                          0
                  5                         0.4709                     1.5291
                  6                         0.1344                     1.5962
                  7                        -0.2448                     1.4860
                  8                        -0.5690                     1.4310
                  9                        -0.2869                     1.3633
                 10                        -0.1972                     1.2685
                 11                        -0.2252                     1.1220
                 12                        -0.0914                     1.4422
                 13                        -0.1942                     1.1241
                 14                        -0.3903                     0.5603
                 15                        -0.4065                     1.0373
                 16                        -0.3045                     1.6325

Composite principal components are calculated using principal components (table4.2) are shown in
table 4.3. Further: S/N ratios are concluded from composite principal components. obtained results
are also presented in table 4.3.

                    Table 4.3: S/N ratios for composite principal components
                                   Composite principal
              Trail no                                                 S/N ratio
                                        component
                 1                         1.0661                       0.5563
                 2                         0.8283                       -1.6357
                 3                         0.4562                       3.4082
                 4                            0                            0
                 5                         1.5999                       4.0822
                 6                         1.6018                       4.0924
                 7                         1.5060                       3.5566
                 8                         1.5399                       3.7502
                 9                         1.3931                       2.8800
                10                         1.2837                       2.1695
                11                         1.1443                       1.1713
                12                         1.4450                       3.1979
                13                         1.1407                       1.1438
                14                         0.6828                       -3.3136
                15                         1.1141                       0.9385
                16                         1.6676                       4.4420


                                               410
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

       The stastical analysis tool ANOVA was used to analyze the contribution of individual factors
on output responses. And respective contributions are presented in table 4.4.

                   Table 4.4: ANOVA analysis for composite quality indicator
        Source of        Sum of            Mean sum
                    DOF                                  F-ratio   %Contribution Rank
      variation(SV)     squares            of squares
       Temperature    3 2.054357             0.6847      18.488          63.25          1
         Pressure     3 0.136679             0.0455       1.230          4.20           4
        Soak time     3  0.41253             0.1375      3.7127          12.70          3
       Cooling type   2  0.0676              0.0338      0.9131          3.12           5
       Composition    3  0.54291             0.1809      4.8861          16.71          2
        Residual      1  0.03703             0.0370
          Total      15




                      Figure 4.1: S/N plots for principal component analysis
                                            f



                                               411
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

       By using S/N ratio, the S/N ratio plots are obtained. As shown in fig4.1.the Optimum levels
of each factor has been concluded and presented in table 4.5.

                       Table 4.5: Optimum levels for each process parameters
            Trail
                      Process parameters        Optimum levels          Optimum values
             no
              1          Temperature                   2                     1750C

              2            Pressure                    4                    10M pa

              3           Soak Time                    1                        0

              4          Cooling type                  3                   Quenched

              5          Composition                   3                      20%



VI. CONCLUSION

        Application of PCA can eliminate multi co linearity of the output responses and transform
these correlated responses into uncorrelated quality indices called principal components. Absence of
correlation between the responses is the basic assumption for applying Taguchi optimization
technique. It can be recommended that the PCA based hybrid Taguchi method is good, for example,
in the case of process (chemical and pharmaceutical) industries when there are hundreds of response
variables. In our experimentation from the previously presented experimental results and analysis
tables it can be concluded that five parameters influencing output responses with varying percentage.
The optimum levels of each factor are temperature at level 2 and the optimum value is 1750C.
Pressure at level 4 and the optimum value is 10M pa. Soak Time at level 1 and the optimum value is
0. Cooling type at level 3 and the optimum value is quenched. Composition at level 3 and the
optimum value is 20% are concluded.

VII. ACKNOWLEDGMENT

       The satisfaction that accompanies the successful completion of any task would be incomplete
without introducing the people who made it possible and whose constant guidance and
encouragement crowns all efforts with success.

       I express my sincere gratitude to and sri R.Umamaheswarrao, Department of Mechanical
Engineering. GMRIT Rajam. , Dr K N S Suman, Assistant professor department of mechanical
engineering, A U, Visakhapatnam

       We are highly indebted to him for his guidance, timely suggestions at every stage and
encouragement to complete this project work successfully.

        Last but not the least we are deeply indebted to our family for all their support and who stood
behind me to get this project completed in time. We are thankful to All Mighty for providing us with
this opportunity.


                                                 412
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

VIII. REFERENCES

 [1]  A Bhatia, R Gupta… - Korea-Australia …, 2007”Compatibility of biodegradable poly (lactic
      acid)(PLA) and poly (butylene succinate)(PBS) blends for packaging application”.
 [2] JT Yeh, CH Tsou, CY Huang, KN Chen… - Journal 2010” Compatible and crystallization
      properties of poly (lactic acid)/poly (butylene adipate‐co‐terephthalate) blends”.
 [3] Mitsugu Todo1 and Tetsuo Takayama2 2007 “Fracture Mechanisms of Biodegradable
      PLAand PLA/PCL Blends”.
 [4] Lu, J., Qiu, Z., and Yang, W., 2007”, Fully biodegradable blends of poly (l-lactide) and poly
      (ethylene succinate): Miscibility, crystallization, and mechanical properties. Polymer. 48:
      4196-4204.
 [5] Hung, S.J., & Edelman, P.G. 1995”, An overview of biodegradable polymers and
      biodegradation of polymers. In G.Scott and D.Gilead (Eds.), Degradable polymers: principles
      and application (pp.8-24). London: Champman and Hall.
 [6] Lee, S. and Lee, J. W., 2005, Characterization and processing of Biodegradable polymer
      blends of poly (lactid acid) with poly (butylenes succinate adipate). Korea-Australia
      Rheology Journal. 17: 71-77.
 [7] Jiang, L., Wolcott, M. P., and Zhang, J., 2006, Study of Biodegradable Polylactide/Poly
      (butylenesadipate-co-terephthalate) Blends. Biomacromolecules. 7: 199-207.
 [8] Todo, M., Park, S. D., Takayama, T., and Arakawa, K., 2007, Fracture micro mechanisms of
      bioabsorbable PLLA/PCL polymer blends. EngFract Mech. 74: 1872-1883.
 [9] Pravin R. Parate and Dr. Ravindra B. Yarasu, “Optimization of Process Parameters of
      Lapping Operation by Taguchi Approach for Surface Roughness of SS 321”, International
      Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 4, 2013,
      pp. 15 - 21, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
 [10] S.Shankar, Dr.H.K.Shivanand and Santhosh Kumar.S, “Experimental Evaluation of Flexural
      Properties of Polymer Matrix Composites”, International Journal of Mechanical Engineering
      & Technology (IJMET), Volume 3, Issue 3, 2012, pp. 504 - 510, ISSN Print: 0976 – 6340,
      ISSN Online: 0976 – 6359.




                                              413

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:0
posted:9/28/2013
language:Latin
pages:13