30120130404044

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

401

IMPROVEMENT OF TOUGHNESS AND STIFFNESS OF BIOPOLYMER

BLENDS USING PCA BASED TAGUCHI APPROACH

R.Umamaheswarrao1, T.VenkataSylaja

2, Dr. K N S Suman

3

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

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 4, Issue 4, July - August (2013), pp. 401-413

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402

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:

( )( ) ( )

( ) ( )kyky

kykykx

ii

iii

min max

min

−

−=

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:



403

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

Then calculate x2, y

2, 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.



404

III. EXPERIMENTATION

A. Materials used

1) POLYLACTIC ACID(PLA)

Table 3.1 Properties of PLA

Fig 3.1 Poly Lactic Acid

2) POLYCAPROLACTANE(PCL)

Table3.2 Properties of PCL

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,160

oC,150

oC, 140

oC and130

oC 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.

Tensile modulus 2.7-16 Gpa

Melting index 8/10 g/min

Density 1.21-1.43 g/cm3

Crystalinity 37% _

Melting index 7/10 g/min

Density 1.02-1.12 g/cm3

Melting point 60 oC



405

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

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

Table 3.4: Taguchi L16 OA design

Process

Parameters

Notation

Units Level 1 Level 2 Level 3 Level 4

Temperature T 0C

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 --

S.NO T P S T 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



406

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



407

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



408

Table 3.5: Taguchi L16 OA design Experimental results

S.NO T P S T CS C

Experimental results

Toughness(J/m2) Stiffness(N/m

2)

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



409

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

S.NO T P S T CS C

NORMALIZED DATA

Toughness(J/m2) Stiffness(N/m

2)

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



410

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

tabulated in table4.2.

Table 4.2: Principal components

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

Trail no Composite principal

component S/N ratio

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

Trail no ψ 1 (1st P.C) ψ 2 (2

nd 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

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July

The stastical analysis tool ANOVA was used to analyze the contribution

on output responses. And respective contributions are presented in table

Table 4.4: ANOVA analysis

Figure 4.1: S/N plots f

Source of

variation(SV) DOF

Sum

squares

Temperature 3 2.054357

Pressure 3 0.136679

Soak time 3 0.41253

Cooling type 2 0.0676

Composition 3 0.54291

Residual 1 0.03703

Total 15

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME

411

ANOVA was used to analyze the contribution of individual factors

on output responses. And respective contributions are presented in table 4.4.

ANOVA analysis for composite quality indicator

S/N plots for principal component analysis

Sum of

squares

Mean sum

of squares F-ratio %Contribution

2.054357 0.6847 18.488 63.25

0.136679 0.0455 1.230 4.20

0.41253 0.1375 3.7127 12.70

0.0676 0.0338 0.9131 3.12

0.54291 0.1809 4.8861 16.71

0.03703 0.0370


August (2013) © IAEME

of individual factors

%Contribution Rank

1

4

3

5

2



412

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

no Process parameters Optimum levels Optimum values

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.



413

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.

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