Gray optimization of process parameters of surface ...

DOI 10.1515/polyeng-2012-0151      J Polym Eng 2013; 33(7): 665–672

Irullappasamy Siva*, Jebas Thangaih Winowlin Jappes, Pandian Pitchipoo, Sandro Campos Amico, Erumaipatty Rajagounder Nagarajan and Nainar Azhagesan

Gray optimization of process parameters of surface modification of coconut sheath reinforced polymer composites Abstract: Surface modification of natural fiber may greatly enhance the mechanical interlocking between fiber and matrix. Although there are many reports on surface modi­ fication of natural fibers, little technical information is available to enable the selection of optimized surface modification conditions. In this work, treatment para­ meters, such as bath temperature, agent concentration, and treatment time, are optimized to achieve higher inter­ facial adhesion. The effect of these parameters on flexural and impact strength is investigated by applying gray rela­ tional techniques. Experimental results show that NaOH concentration and treatment time are significant variables which improve interfacial strength, while NaOH bath tem­ perature appears less important. Keywords: alkali treatment; coconut sheath; gray method; polymer composite; process parameters. *Corresponding author: Irullappasamy Siva, Centre for Composite Materials, Department of Mechanical Engineering, Kalasalingam University, Krishnankoil-626126, India, e-mail: [email protected] Jebas Thangaih Winowlin Jappes and Nainar Azhagesan: CAPE Institute of Technology, Department of Mechanical Engineering, Tirunelveli-627114, India Pandian Pitchipoo: Department of Mechanical Engineering, PSR Engineering College, Sivakasi-626140, India Sandro Campos Amico: Department of Materials Engineering, UFRGS, Porto Alegre/RS, Brazil Erumaipatty Rajagounder Nagarajan: Department of Chemistry, Kalasalingam University, Krishnankoil-626126, India

1 Introduction Natural fibers are found to be great competitors for syn­ thetic fibers, due to their low density, lower CO2 emission, and their eco-friendly intrinsic characteristics [1, 2]. Recent research work indicates the capability of natural fibers extracted from various parts of a plant to show similar performances to existing synthetic fibers [3]. Automobile

and other industrial sectors are currently adopting those eco-friendly composites in their end products [4]. One of the great challenges to researchers is to develop compatibility between natural fibers and the polymer matrix. Surface energy of natural fibers is low, because of their hydrophilic nature [2]. In lignocellulosic fibers, cellulose is a crystalline material which gener­ ally plays a role in load bearing. The matrix is the bulk material phase which makes the fibers work as an inte­ gral unit by binding them. The applied load is transferred from the matrix to the fiber through their interfaces [2]. In nature, cellulosic fibers are covered with lignin, pectin, and wax, among others, which prevents an effective stress transfer when they are in a composite. Alkali treatment is one of the suitable surface modification processes which removes the deposits and activates the cellulose [4]. The fiber then becomes less hydrophilic, promoting stronger mechanical interlocking with the matrix. Rout et al. [5] studied the surface modification of coir fibers collected from the husk of the coconut fruit and their use in polyester composites. Bleaching, alkali treat­ ment, and acrylonitrile grafting were done on coir fibers. The concentration of alkali solution/acrylonitrile graft­ ing and temperature of the bleach, were found to greatly influence the tensile and flexural properties differently. Park and Kim [6] studied the effect of temperature of the surface activation process for carbon fiber composites, in order to reduce the sorption properties. They concluded that, the surface developed acidic properties when the temperature was   900°C. John et al. [7] investigated the effect of surface treatment conditions on the mechani­ cal properties of sisal/glass polyester hybrid composites. They used 0.5%, 1%, 2%, and 4% alkali solutions, and the latter yielded the highest tensile strength. None of the researchers reported specific treatment conditions or any optimization technique for the novel coconut sheath reinforcement. Recently, most manufacturing processes involved in producing polymer composites were optimized in order to achieve superior quality [8–11]. Chang et  al. [12] used

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666      I. Siva et al.: Gray optimization of process parameters the L18 Taguchi technique to optimize injection pressure, compression pressure, and mold pre-heat temperatures, aiming to produce high quality Resin Transfer Molding products. Das et al. [13] optimized the fiber content ratio among jute, sisal, and coir fibers used as reinforcements for a polypropylene matrix, obtaining optimum tensile properties for a composite made of about 66% sisal fibers and 34% coir fibers. Qiao et  al. [14] presented a global approximation method to optimize material architecture in fiber reinforced plastic beams. For the optimization, deflection limit of the composite beam, material failure and elastic buckling were considered as constraints. Hence, in this work, an L9 orthogonal array with three parameters (bath temperature, concentration, treat­ ment time) in three levels was used to optimize the NaOH alkali treatment conditions, in order to achieve greater flexural and impact strength. Further, the gray relational technique [15] was adopted, since this study has multiple objective functions.

Table 1 Comparison of characteristics of novel coconut sheath and conventional coir fibers. Properties

Coconut sheath Coir fiber (Experimental data) [16, 17]

Wax content (%) Density at room temperature (g/cc) Cellulose content (%) Lignin content (%) Moisture content (%) Ash content (%)

0.41 1.38 68.36 20.63 8.79 1.04

– 1.15 32 29.23 8 1.5

control factors were designed with three levels, as shown in Table 2. The selected L9 matrix is shown in Table 3. In general, the dried fibers were immersed into the NaOH alkali solution, followed by thorough removal of the alkali solution from the fibers surface by washing.

2.3 Optimization of surface modification

2 Materials and methods Unsaturated polyester (USP)-(Grade: SBA2303-Isothalic) was used as the matrix. Methyl ethyl ketone peroxide (MEKP) was used as the catalyst and cobalt naphthen­ ate as the accelerator; all those chemicals includes were supplied by Vasavibala Resins-Chennai, Tamilnadu StateIndia. NaOH (AR Grade) was supplied by United Scientific Suppliers, Madurai, Tamilnadu State-India. Naturally woven coconut sheaths was acquired from local agricul­ ture fields in Tamilnadu region, India.

2.1 Properties of coconut sheath Naturally woven coconut sheath can be found in the bottom portion of coconut trees. These mats contain uni­ directional strong fibers and lean linked fibers. These fibers are different from ordinary coir fibers, for which many references are available in the literature [5, 16, 17]. Table 1 shows the chemical characterization of novel coconut sheath and conventional coir fibers.

2.2 Surface modification The coconut sheath was initially battered with cold water and then dried. The fibers were then surface treated under different conditions. In this study, the three chosen

Gray relational analysis was used to maximize mechanical strength. This theory is especially suitable for data with uncertain, multi inputs and discrete properties [15]. The steps below were followed while applying gray relational analysis: –– Step 1: Normalize the experimental results of flexural strength and impact strength for all the trials –– Step 2: Calculate the gray relational coefficient –– Step 3: Calculate the gray relational grade –– Step 4: Perform statistical analysis of variance (ANOVA) –– Step 5: Select the optimal levels of process parameters –– Step 6: Conduct confirmation experiment and verify the optimal process parameters setting.

2.4 Fabrication and testing The compression molding technique was used for the production of coconut sheath-reinforced polyester Table 2 Factors and factor levels selected in the matrix experiment. Factor

A. NaOH bath concentration (normality) B. Treatment time (h) C. Treatment temperature (°C)

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Level 1

2

3

0.5 1 30

1.0 5 60

1.5 10 90

I. Siva et al.: Gray optimization of process parameters      667

Table 3 L9 matrix used. Expt. no. 1 2 3 4 5 6 7 8 9

Column numbers and factor assignment A

B

C

1 1 1 2 2 2 3 3 3

1 2 3 1 2 3 1 2 3

1 2 3 2 3 1 3 1 2

composites, based on previous experience of the group. For that, the coconut sheath was cut to dimensions of 180 mm × 150 mm × 3 mm. The formulated resin was cast into the empty mold cavity and six coconut sheath layers (opti­ mized overall fiber weight fraction of 0.45) were stacked on top of that. An optimized pressure of 50 kg/cm2 was then applied and the material was allowed to cure for 12 h. The composites were taken out of the mold and cut into the dimensions recommended by ASTM D790 stand­ ard for flexural testing and ASTM D256 for impact testing.

3 Results and discussion 3.1 Taguchi method Experimental data of flexural and impact strength cor­ responding to L9 orthogonal array and their signal-tonoise (S/N) ratios, are displayed in Table 4. Taguchi methods use the S/N ratio to analyze the experimental results, because this ratio represents both the average

(mean) and the variation (scatter) of these results. This method stresses the importance of studying the response variation using the S/N ratio, resulting in minimization of quality characteristic variation due to uncontrollable parameters. S/N ratios are calculated based on the smaller the better and the larger the better concepts, depending on the quality parameters. Since the output parameters of this study are flexural and impact strengths, the S/N ratio is calculated using the larger the better concept, which is shown in Eq. (1): 1 n 1 S = -10 log  ∑ i=1 2  N yi  n





(1)

where i = experiment number, n = number of experiments, and yi = value of parameter for the ith experiment.

3.2 Gray relational analysis Gray relational analysis is working based on the gray system theory, which was proposed by Deng [15]. The major advantage of the gray theory is that it is suitable to handle both incomplete information and unclear problems. It is used as an analysis tool when there is not enough data. It was recognized that the gray relational analysis is largely applied in the area project selection, prediction analysis, and performance evaluation etc. Gray relational analysis uses information from the gray system to dynamically compare each factor quantitatively. Gray relational analy­ sis is a method to analyze the relational grade for discrete sequences. This is unlike the traditional statistics analysis handling the relation between variables. The statistical analysis works with plenty of data and the data distribu­ tion must be typical. However, gray relational analysis

Table 4 Experimental data and their response. Exp. no.

1 2 3 4 5 6 7 8 9 Confirmation experiment

Experimental data

S/N ratios

Flexural strength (MPa)

Impact strength (J)

Flexural strength

Impact strength

1198.9 1541.7 513.9 410.3 1552.5 263.1 448.3 1155.6 53.3 1652.12

1.01 1.22 4.17 6.48 1.32 6.34 6.63 1.03 1.91 14.36

61.58 63.76 54.22 52.26 63.82 48.40 53.03 61.26 34.54 –

0.10 1.76 12.41 16.23 2.39 16.05 16.43 0.28 5.63 –

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668      I. Siva et al.: Gray optimization of process parameters requires less data and can analyze many factors that can overcome the disadvantages of statistics methods.

Table 6 Gray relational coefficient values. Exp. no.

3.2.1 N  ormalizing the flexural strength and impact strength for all the trials The first step is to normalize the S/N ratio of the experi­ mental data. Normalization is a transformation performed on a single data input, to distribute the data evenly and to scale it into an acceptable range for further analysis. In this work, the flexural strength and impact strength are normalized using the larger the better concept shown in Eq. (2) and the values are compiled in Table 5: X ( j)= * i



xi ( j )-minxi ( j )

max xi ( j )-minxi ( j )

(2) 

where xi(  j ) is the S/N ratio of ith experimental results in the jth experiment; i = 1, 2,…, n. j = 1, 2,…, m.

From the normalized data, the gray relational coefficient is calculated to express the relationship between the best and the actual results by using Eq. (3) and the results are displayed in Table 6. γi ( j ) =



∆ min+ ξ∆ max ∆ i ( j ) + ξ∆ max

Table 5 Normalized S/N values.

1 2 3 4 5 6 7 8 9

Impact strength

0.87 1.00 0.60 0.56 1.00 0.49 0.58 0.85 0.33

0.33 0.36 0.67 0.98 0.37 0.96 1.00 0.34 0.43

3.2.3 Calculation of the gray relational grade The gray relational grade is calculated using Eq. (4). The computed gray relational grade and the ranking of the experiments are given in Table 7. 3

Γ′ i = ∑ [ Wi ( j ) × γi ( j )] j=1

(4)



where Wi(j) = weightage of parameter j. In this work, the weights of the parameters were found in two different ways. Firstly, equal weights were assigned to the parameters and the gray relational grade was calculated [18, 19]. Secondly, weights were calculated using entropy measurement [20].

(3) 

Xo* ( j )  = ­referential where ∆ i ( j ) = abs( Xo* ( j )-Xi* ( j )); * Xi* ( j )  =  j = 1,  2,…,m; se­ quence value ( Xo ( j ) = 1; ∆ min= min min ∆i ( j ); specific comparison value; ­ i j ∆ max = max max ∆i ( j ); and ξ = distinguished coefficient i j ξε[0, 1].

Exp. no.

Flexural strength 1 2 3 4 5 6 7 8 9

3.2.2 Calculation of the gray relational coefficient

Gray relation coefficient

Normalized S/N

3.2.4 Determination of weights using entropy The entropy is a measure of the uncertainty associated with a random variable of the expected information content of a certain message, where the uncertainty is represented by a discrete probability distribution [21]. The weights of the criteria are determined using the following steps [22]: Table 7 Gray relational grade.

Flexural strength

Impact strength

Exp. no

Based on equal weights

Based on entropy

Rank

0.92 1.00 0.67 0.61 1.00 0.47 0.63 0.91 0.00

0.00 0.10 0.75 0.99 0.14 0.98 1.00 0.01 0.34

1 2 3 4 5 6 7 8 9

0.97931 0.98168 0.99312 0.99766 0.98245 0.99674 0.99806 0.97946 0.98117

0.97891 0.98132 0.99306 0.99770 0.98209 0.99679 0.99810 0.97907 0.98101

7 5 6 2 4 3 1 8 9

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I. Siva et al.: Gray optimization of process parameters      669

–– Formulation of normalized pay-off matrix (Pij): The experimental data (Table 4) were normalized using Eq. (5). This is called normalized pay-off matrix (Pij).



 N 11 N 12   N 21 N 22 … … Pij =  … …  .   N m 1 N m 2

… N 1n   … N 2n  … …  for … …   … N mn 

N ij =

Xij

∑

m

i= 1

Xij (5)

where xi(  j ) = jth parameter value for the ith experiment. –– Determination of entropy: The entropy Ej of the set of experiments for parameter j from the normalized pay-off matrix (Pij) is determined by using Eq. (6):



Ej =

m 1 p ln( pij ) ∑ ln( m ) i=1 ij



(6)

where m = the number of experiments. –– Determination of degree of diversification: Next, the degree of diversification of the information provided by the outcomes of the parameter j is determined using Eq. (7):

Dj = 1-Ej 

(7)

–– Determination of weights of parameter: Finally, the weights of flexural and impact strength were calculated using Eq. (8), as 0.49 and 0.51, respectively. These weights were used to compute the gray relational grade: Wi ( j ) =

Dj

∑

(8)

n

D j =1 j

Table 8 Main effects of gray grade. 1

3 Max-Min

NaOH concentration A 0.98470 0.99228 0.98623 0.00758 Treatment time B 0.99168 0.98120 0.99035 0.01048 Treatment temperature C 0.98517 0.98684 0.99121 0.00604

24]. From these results, the optimal parameter setting for maximizing flexural strength and impact strength simul­ taneously, is to maintain the NaOH concentration at level 2, treatment time at level 1, and treatment temperature at level 3. Thus, analysis of the S/N ratio gave the optimum surface modification condition as A2B1C3. That is, the 1 normality NaOH solution with treatment time of 1 h in a 90°C hot bath. The results of ANOVA for gray relational grade values are shown in Table 9. These results show that treatment time was the most significant parameter followed by NaOH concentration, whereas treatment temperature appeared less important.

3.2.6 Confirmation experiment Based on the above computations, it is identified that the optimal conditions for treating the novel coconut sheath to yield superior flexural and impact strength of the compos­ ites are A2, B1 and C3. This prediction was verified through a confirmation test. A new composite was molded as per the predicted conditions and the experimental results (also included in Table 4) showed higher flexural strength (1652.12 MPa) and impact strength (14.36 J) compared to the previous nine experiments.



The computed gray relational grades based on the two used approaches are shown in Table 7. It can be seen that the ranking orders in both techniques are the same. In order to confirm the computation of the gray relational grade, the weights were determined using entropy.

3.2.5 Performing statistical analysis of variance (ANOVA) ANOVA is conducted to determine which parameter signif­ icantly affects the performance characteristics. The main effects of the gray grades are displayed in Table 8 and Figure 1. Basically, the larger the gray relational grade, the better the multiple performance characteristics [19, 23,

2

Figure 1 Main effects of gray grade.

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670      I. Siva et al.: Gray optimization of process parameters Table 9 ANOVA table for flexural and impact strength. Factor NaOH concentration Treatment time Treatment temperature

DoF

SoS

Mean

Contribution

%

2 2 2

0.00010 0.00020 0.00006

0.00005 0.00010 0.00003

0.27536 0.55789 0.16675

27.54 55.79 16.67

DoF, Degree of Freedom; SoS, Sum of Squares.

Further, properties of untreated and treated samples (optimal conditions) were investigated through some characterizing techniques and the results are shown in Table 10 and Figure 2. The wax content of the coconut sheath was considerably removed after surface modifica­ tion. In many reports in the literature [25, 26], removal of the waxy layer is considered to be one of the main reasons to justify the enhancement of the mechanical interlocking between fiber and matrix. Hence, this could be the reason for the enhanced performance of the treated fiber rein­ forced composite. It can be understood from the property comparison of untreated and optimally treated fiber (Table 10), that the removal of wax from the natural fiber may promote the mechanical interlocking and ultimately the mechani­ cal performance of the composites. The results also show a decrease in cellulose and an increase in lignin content. The nonreactive hydroxyl group present in cellulose is broken by the alkali solution, resulting in the decrease in cellulose content, and in the comparative increase in lignin content. The FT-IR spectra of both NaOH-treated and untreated coconut sheath fibers are shown in Figure 2. It is observed that the strong broad band which appeared at 3100– 3700 cm-1 is due to the presence of a hydroxyl group after surface modification. It is also revealed that intermolecu­ lar and intramolecular hydrogen bonded O-H stretching vibrations had the same absorbance for the untreated fiber, i.e., no significant shift after alkali treatment as

mentioned in the literature [27–29]. A band close to 2921 cm-1 is assigned to CH2 symmetrical stretching in both cases. The intense peak, along with a shoulder observed at 1020–1060 cm-1, corresponds to C-OH stretching and C-O-C bridging asymmetry. The bands appearing in the 1250–1700  cm-1 region are due to OH in plane bending, OH bending due to bound water, CH2 wagging in plane, CH deformation stretch, and CH2 wagging. The peak at 896  cm-1 is indicative of the plane mode of C-O-C ring asymmetrical stretching. The vibration at 669 cm-1 can be interpreted as the out-of-plane mode of OH vibration [3]. Many of the FT-IR bands shifted to a higher wave number (by 3–6 cm-1) in NaOH-treated coconut sheath. However, a few bands were shifted to a lower wave number (by 3–5 cm-1). The absorbance measured for all peaks increased in the NaOH-treated coconut fiber. The signifi­ cant broadening of the band at around 3100–3700  cm-1 and increasing intensity in NaOH-treated coconut sheath, ascribed to an increase in the hydroxyl groups, made the fiber more hydrophilic. The overall trends, such as shifting in wave numbers, increased absorbance, and broadening of peaks indicate an increase in crystallinity of NaOHtreated coconut sheath compared with untreated fiber. For a better understanding, SEM images of the frac­ tured regions are presented in Figure 3A and B. For the

Table 10 Property comparison of untreated novel coconut sheath (CS) and treated CS under optimal conditions. Properties

Wax content (%) Density at room temperature (g/cc) Cellulose content (%) Lignin content (%) Moisture content (%) Ash content (%) USP, Unsaturated Polyester.

Untreated CS/reinforced USP

Treated CS/reinforced USP

0.41 1.38 68.36 20.63 8.79 1.04

0.12 0.98 52.89 33.29 12.66 5.35

Figure 2 FT-IR spectra comparison of untreated CS (UT) reinforced USP composite with CS treated under optimal conditions (NT) USP composite.

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I. Siva et al.: Gray optimization of process parameters      671

A

B

Figure 3 (A) SEM image of the tensile fractured untreated CS/USP composite; (B) SEM image of the NaOH treated CS/USP composite.

untreated coconut sheath composite, a gap between the fiber and the matrix could be noticed, which represents poor bonding and fiber pull out in the fractured region. Further, the surface of the matrix looks smooth due to the presence of the waxy layer, indicating ineffective stress transfer between the fibers and the matrix. By contrast, a rougher surface could be seen in the micrograph of NaOHtreated coconut sheath reinforced composite. All of these findings ratify the previously shown strength results.

multiple objectives into a single objective function (gray relational grade) to support the investigation of the Taguchi method. First equal individual response weights for the parameters were assigned to compute gray rela­ tional grade and it was later confirmed by the weights determined by an entropy measurement technique. It is concluded that the 1  N concentration of the NaOH bath, 1  h treatment time, and 90°C NaOH bath temperature, would be the optimum surface modification conditions for the novel coconut sheath reinforcement.

4 Conclusions

Acknowledgments: The authors wish to thank the Depart­ ment of Science and Technology, India for the funding and also the Center for Composite Materials and Depart­ ment of Mechanical Engineering, Kalasalingam University for their kind permission to carry out the preparation and testing of the composites.

In this study, the influence of NaOH concentration, treat­ ment time, and NaOH bath temperature on the strength of coconut sheath reinforced polymer composites was analyzed. For the optimization of these process param­ eters, the gray based Taguchi method was proposed. The gray relational technique was recommended to convert

Received November 15, 2012; accepted July 30, 2013; previously published online August 30, 2013

References [1] Ibraheem SA, Aidy A, Khalina A. Polym.-Plast. Technol. Eng. 2011, 50, 613–621. [2] Bledzki AV, Gassan J. Prog. Polym. Sci. 1999, 24, 224–274. [3] Onal L, Karaduman Y. J. Compos. Mater. 2009, 43, 1751–1768. [4] Ahmad SH, Rasid R, Bonnia NN, Zainol I, Mamun AA, Bledzki AK, Beg MDH. J. Compos. Mater. 2011, 45, 203–217. [5] Rout J, Misra M, Tripathy SS, Nayak SK, Mohanty AK. Compos. Sci. Technol. 2001, 61, 1303–1310.

[6] Park S-J, Kim K-D. Carbon 2001, 39, 1741–1746. [7] John MJ, Francis B, Varughese KT, Thomas S. Composites: Part A 2008, 39, 352–363. [8] Park CH, Lee WI. J. Compos. Mater. 2005, 39, 347–374. [9] Kiran Kumar P, Raghavendra NV, Sridhara BK. J. Compos. Mater. 2012, 46, 549–556. [10] Aleksendric D, Senatore A. J. Compos. Mater. 2012, 46, 2777–2791.

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672      I. Siva et al.: Gray optimization of process parameters [11] Lin CL. Mater. Manuf. Processes 2004, 19, 209–220. [12] Chang C-Y, Hourng L-W, Chou T-Y. J. Reinf. Plast. Compos. 2006, 25, 1027–1037. [13] Das D, Mukhopadhyay S, Kaur H. J. Compos. Mater. 2012, 46, 3311–3319. [14] Qiao P, Davalos JF, Barbero EJ. J. Compos. Mater. 1998, 32, 177–196. [15] Deng JL. J. Grey System 1989, 1, 1–24. [16] Silva GG, Souza DD, Machado J, Hourston D. J. Appl. Polym. Sci. 2000, 76, 1197–1206. [17] Rahman MM, Khan MA. Compos. Sci. Technol. 2007, 67, 2369–2376. [18] Satapathy BK, Bijwe J, Kolluri DK. J. Compos. Mater. 2006, 40, 483–501. [19] Rajini N, Jappes JTW, Rajakarunakaran S, Siva I. J. Polym. Eng. 2012, 32, 555–566. [20] Jee D-H, Kang K-J. Mater. Des. 2000, 21, 199–206.

[21] Hwang C-L, Yoon K. Multiple Attribute Decision Making: Methods and Applications: A State-of-the-Art Survey. SpringerVerlag: Berlin Heidelberg, New York, 1981. [22] Pomerol J-C, Barba-Romero S. Multicriterion Decision in Management: Principles and Practice. Springer: Springer Science+Business Media, LLC, 2000. [23] Ahilan C, Kumanan S, Sivakumaran N. Adv. Prod. Eng. Manage. 2010, 5, 171–180. [24] Narender Singh P, Raghukandan K, Pai BC. J. Mater. Process. Technol. 2004, 155–156, 1658–1661. [25] Brahmakumar M, Pavithran C, Pillai R. Compos. Sci. Technol. 2005, 65, 563–569. [26] Cantero G, Arbelaiz A, Llano-Ponte R, Mondragon I. Compos. Sci. Technol. 2003, 63, 1247–1254. [27] Sinha E, Rout SK. Bull. Mater. Sci. 2009, 32, 65–76. [28] Sinha E, Rout SK. J. Mater. Sci. 2008, 43, 2590–2601. [29] Jannah M, Mariatti M, Bakker A, Khalil A. J. Reinf. Plast. Compos. 2009, 28, 1519–1532.

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