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Abstract

The main objective and the originality of this work are to create a hybrid natural/natural fiber bio-composite by Response Surface Methodology RMS technique based on statistical analysis of variance ANOVA by using the properties of the individual fiber bio-composites. Hybrid bio-composites are manufactured by combining two or more dissimilar kinds of fiber in a single continuous phase. In the first section, Response Surface Methodology using a Box-Behnken experimental design and the Analysis of Variance are applied to investigate the effects of the type of fibers, the type of chemical treatment, the volume fraction of fiber and the treatment time on the tensile properties namely, ultimate tensile strength and Young’s modulus in the tensile quasi-static loading when used two resins namely, epoxy and polyester. In the studied range, statistical analysis of the results showed that selected variables had a significant effect on the mechanical properties, except the treatment time that has a very weak significance effect on the mechanical properties. Then, to maximize the mechanical properties, the optimal conditions coded by RSM were found: the type of fibers of [−0.28 and −0.33], the chemical treatment of −1, the volume fraction of fiber of 1 and the treatment duration of [−0.97 and −1] for epoxy resin matrix. Similarly, when used the polyester resin matrix; the type of fibers of −0.26, the chemical treatment of −1, the volume fraction of 0.99 and the sinking time of [−0.94 and −0.93]. The obtained optimum parameters were confirmed experimentally in the second section.

Keywords

Natural fibers hybrid bio-composites response surface methodology modelling optimization

Introduction

Natural fibers have been used to reinforce materials for over 3000 years. More recently, the natural fiber composites are receiving more attention during the last 30 years due to their ecologically friendly behavior.¹ Researchers focus their investigations on composite materials based on renewable resources with comparable properties to synthetic fibers for reducing petroleum consumption and pollution.^2–6 The natural fibers are lighter than synthetic fibers which revert as fuel reduction when this material is used by the automotive industry. Moreover, the natural fibers have low density, low cost, its availability, renewability, easy recyclability, low process energy, good thermal and acoustical insulation properties, acceptable specific strength and modulus, and reduced tool wear (non-abrasive) in machining operations.^7–10 Table 1 shows physical properties of some natural fibers used as reinforcement in composites materials.^11–16

Table 1.

Physical and mechanical properties of sisal, jute, flax fibers and resins used in this study.

Gage length (mm)	Density (g/cm³)	Average ultimate tensile strength (MPa)	Average Young’s modulus (GPa)	Average elongation at break (%)	Diameter (µm)	Ref.
a) Jute fiber						This work
10	1.30	52.97	0.7200	13.86	800-960
30		31.95	0.2038	20.91
50		29.02	0.3750	14.28
b) Sisal fiber
10	1.48	248.02	3.0060	13.80	220-260
30		212.36	8.8500	04.85
50		242.28	9.0160	3.68
c) Flax fiber
10	1.50	216.93	14.8800	2.09	16-18
d) Resins
Epoxy resin	–	3.6310	2.1342	9.1273	–
Polyester resin	–	17.3655	0.8559	3.6685	–

The properties of the composite depend not only on the properties of the fiber but are also controlled by the properties of the matrix, interfacial adhesion between the fiber and matrix and the design of the hybrid system. In order to improve their properties researchers turned their focus on the study of effect on mechanical properties due to hybridization of artificial–artificial, natural–artificial and natural–natural fiber types.¹⁷ Hybrid composites consist of an amalgamation of two or more fibers in a polymer matrix. Recently, hybrid composites materials have gained some attention from researchers to study their behavior in the materials field as it shifts from composites to the development of hybrid composites and addressed the competitiveness, capabilities and suitability of hybrid natural–natural fibers as reinforced fillers in polymeric matrices.

Schneider and Karmaker¹⁸ developed composites using jute and kenaf fiber and polypropylene resins and they reported that jute fiber provides better mechanical properties than kenaf fiber. Recently, Venkateswaran¹⁹ reported that sisal/banana hybrid natural fiber composite specimens were prepared with different rations by taking 0.4 vol fraction and tensile properties of these hybrid natural fiber composites are also examined using rule of mixtures (RoHM). In the next study, Venkateswaran²⁰ studied the mechanical properties such as tensile strength, flexural strength, impact strength and water absorption rate of sisal and banana fibers reinforced epoxy composite materials. They have observed when the hybridization of the sisal fiber with banana/epoxy composites up to 50% by weight increases the mechanical properties and also decreases the water absorption properties. Boopalan²¹ studied the mechanical and thermal properties of jute and banana fiber reinforced epoxy hybrid composites. Jute fiber was hybridized with banana fiber, in order to enhance the better mechanical properties of composites.

In other words, the adhesion between the reinforcing fibers and the matrix plays an important role in the final mechanical properties of the materials; when natural fiber used as reinforcement in composite materials, many problems occur at the interface due to incompatibility. Fiber -matrix interaction can be improved by surface or structural modification of the fibers using various processes such as alkali treatment, bleaching, acetylation and steaming. It is worth to mention that the chemical treatment of the fibers can either increase or decrease the strength of the fibers, and hence good understanding of what occurs structurally is require.²²

In this paper, two types of chemical treatment namely, alkaline (sodium hydroxide) NaOH and sodium bicarbonate NaHCO₃ were applied to modify the surface characteristics the raw of the flax, jute and sisal fibers. In alkaline treatment, fibers are immersed in NaOH solution for a given period of time. Ray²³ and Mishra²⁴ treated jute and sisal fibers with 5% aqueous NaOH solution for 2 h up to 72 h at room temperature. Similar treatments were attempted by Morrison²⁵ to treat flax fiber. Asumani²² studied the alkali and silane treated kenaf fiber reinforced polypropylene composites. It has been noted that the tensile strength and modulus increased significantly by 25% and 11% respectively after treatment with 5% alkali. Another, Rajesh and Prasad²⁶ studied short jute fiber/PLA composites with different concentrations of NaOH and H₂O₂ treatments on jute fibers. The effect of fiber loading and alkali concentrations used for fiber treatment on the mechanical properties of the composites were investigated. It was reported that the tensile properties of composites with treated fiber at higher fiber loadings were better than those of untreated fiber.

Our study aims to develop models, many researchers in various fields have identified the application of statistical optimization as the best approach for time saving and accurate results in modeling and optimization of many engineering and scientific processes. For example, Rasyid et al.,^26,27 have optimized the conditions for non-woven flax fiber reinforced acodur bio-composites while considering moisture content, curing time and temperature as input variables and flexural strength and modulus as response variables. Similarly, Benkhelladi et al.²⁸ applied desirability function-based multi-objective optimization for hybrid natural/natural bio-composites to obtain the highest mechanical properties.

Although we mentioned many studies about bio-composites, there are few studies in literature studied the mechanical properties of natural–natural hybrid fiber reinforced polymer bio-composites, i.e. The main goals of the first part of this work are to prepare natural hybrid bio-composite plates using response surface methodology (RSM) and the desirability function approach. Then, the ANOVA study involves the effects of input parameters, namely and coded; type of fiber (X₁), chemical treatment (X₂), volume fraction of fiber (X₃) and treatment duration (X₄) on mechanical properties of bio-composites. In the second part of this work, the different tests realized to characterize the mechanical properties of the optimal natural hybrid bio-composite reinforced with polyester or with epoxy resins which were experimentally analyzed.

Materials and methods

Materials

In this present investigation sisal (Agave sisalana), jute (Corchorus Capsularis of Tiliaceae) and flax (Linum usitatissimum) fibers are used for fabricating the bio-composite specimens. All of the materials employed in this work were obtained from commercial sources and used as received. The definitions of fibers are discussed in detail as follows:

Jute fiber

Jute is a bast fiber whose scientific name is Corchorus Capsularis of Tiliaceae family. Jute is a natural biodegradable fiber with advantages such as high tensile strength, excellent thermal conductivity, and coolness etc. Its abundance in availability with cheaper cost has acquired importance of its use in polymer composites.²⁹ Jute fiber extracted from the bark of jute plant has three major categories of chemical compounds namely cellulose (58–63 wt%), hemicellulose (20–24 wt%), and lignin (12–15 wt%) and some other small quantities of components like fats, pectins, aqueous extracts, etc.³⁰

Sisal fiber

Natural sisal fiber is a hard fiber extracted from the leaves of the sisal plant in the form of long fiber bundle. This plant, scientifically named A. sisalana Perrine, is of Mexican origin and is grown in Brazil, East Africa particularly in Tanzania, Haiti, India, Indonesia and Thailand.⁴ A sisal plant produces about 200–250 leaves and each leaf contains 1000 ± 1200 fiber bundles which are composed of 4 wt% fiber, 0.75 wt% cuticle, 8 wt% dry matter and 87.25 wt% water. So normally, a leaf weighing about 600 g will yield about 3% by weight of fiber with each leaf containing about 1000 fibers. Sisal fibers with excellent mechanical property are mainly used as textiles, strings, mats, yarns, art ware and reinforced material.³¹

Flax fiber

Flax, L. usitatissimum, belongs to the best fibers. It is grown in temperate regions and is one of the oldest fiber crops in the world. It’s an 80 to 120 cm high plant which possesses strong fibers all along its stem and contains 70% of cellulose. These cellulose based fibers have low density, good tensile strength, stiffness and high aspect ratio.^32,33

Fiber preparation methods

In order to improve the interfacial properties between the fibers (the sisal, jute and flax fibers) and the matrix, we were subjected to several surface treatments. The fibers were cut into 300 ±2 mm long pieces, washed with distilled water and oven dried at 45°C until obtaining a constant weight. In this study, fibers were treated with sodium hydroxide NaOH and sodium bicarbonate NaHCO₃, with various times 4, 12 and 24 h.

The volume fraction of fiber (VF) is calculated by using the following relation.³⁴

V F = \frac{(W_{f} / ρ_{f})}{(W_{f} / ρ_{f}) + (W_{m} / ρ_{m})}

(1)VF is fiber volume fraction, W_f are the weight (g) of fibers (sisal, jute and flax) and W_m is the weight (g) of matrix, ρ_f and ρ_m are the density (g/cm³) of fibers and matrix, respectively. Also, the diameters of sisal, jute and flax fibers were evaluated by a Visual machine 250 tool makers microscope with ×4.5 magnifications and 1 μm resolution at three different random locations along the single fiber and the average diameter was calculated assuming a quasi-cylindrical shape the average value is taken, as shown in Figure 1. The average diameters detected for 100 samples of sisal, jute and flax fibers were 240 ± 40 µm, 880 ± 80 µm and 17 ± 10 µm, respectively.

Figure 1.

Optical microscopy image of a longitudinal sisal, jute and flax fibers.

Mechanical tensile properties of individual fibers

The mechanical tensile properties (ultimate tensile strength, elongation at break and Young’s modulus) of the sisal, jute and flax fibers at various gauge length (GL) have been determined following the ASTM D3822-07 standard, using a Universal Testing Machine (model EZ20; Lloyd Instruments Ltd, UK), equipped with a load cell of 20 kN. The fibers underwent testing under standard conditions of 24°C and 60 % relative humidity. Each fiber was mounted into the machine grips individually, using sandpaper. The tests were conducted using a crosshead speed of 1 mm/min until rupture occurred. Table 1 shows the average values of physical and mechanical properties real of natural fibers (sisal, jute and flax) and resins (epoxy and polyester) used in this study. 20 identical specimens from each fiber and resin were tested and the average value is tabulated.

Treatment with NaOH

In this process untreated sisal, jute and flax fibers, they were respectively immersed in 7, 9 and 1 wt% NaOH solution for various times 4, 12 and 24 h at room temperature. Then, the fibers were washed several times with fresh water to remove any NaOH sticking on the fiber surface, neutralized with dilute acetic acid and after that, washed again with distilled water. Finally, pH was maintained at 7. The fibers were then dried at room temperature until a constant weight was reached.

Treatment with NaHCO₃

Similarly, the second treatment method consisted of soaking the raw of the sisal, jute and flax fibers in 25, 25 and 10 wt% NaHCO₃ solution for various times 4, 12 and 24 h at room temperature, respectively. The fibers were then taken out of the solution, drained, and washed several times with tap water to remove any residual NaHCO₃ traces sticking on the fiber surface. Then, fibers were neutralised with dilute acetic acid. After that, rinsed again with distilled water. Finally, the fibers were dried at room temperature until a constant weight was reached.

Scanning electron microscopy studies

The scanning electron microscopy (SEM) technique is an excellent tool for examining the surface morphology of fibers, both treated and untreated. Examining the surface morphology of fibers is essential to evaluate their potential as effective reinforcement and their resistance to fiber pullout. The SEM photographs of morphology in diameter direction of both untreated and treated surfaces specimens were examined with the help of Scanning Electron Microscopy (Model: JSM 6360LV). The longitudinal topographic surface of the fibers before and after chemical treatment with sodium bicarbonate at various times are presented in Figures 2–4. The surface of the all fibers becomes smoother after the chemical treatment due to the elimination of impurities from the fiber external surface as compared to untreated fiber without treatment. It also improves the natural fiber’s mechanical properties and the interface adhesion of the fiber and the polymer when used to develop polymer composites.^35,36

Figure 2.

Scanning Electron Micrographs of flax fiber: (a) untreated (b) NaHCO₃ (c) NaOH.

Figure 3.

Scanning Electron Micrographs of sisal fiber: (a) untreated (b) NaHCO₃ (c) NaOH.

Figure 4.

Scanning Electron Micrographs of jute fiber: (a) untreated (b) NaHCO₃ (c) NaOH.

Mechanical properties of bio-composite

In order to evaluate the effect of the fibers’ type, chemical treatment, volume fraction and treatment time on the mechanical properties (ultimate tensile strength and Young’s modulus) the modified bio-composites were measured using a Universal Testing Machine (model EZ20; Lloyd Instruments Ltd, UK), equipped with a load cell of 20 kN. The clamps used during the tests have self-concentric alignment and are manually adjusted by mechanical springs. The tensile static tests were performed at a constant speed of 2 mm/min and the longitudinal strain was measured using an extensometer with 30 mm gauge length. All tests were conducted at a room temperature of 26°C and a relative humidity of approximately 30%. Tensile tests were conducted according to the ASTM D 3822-01 specifications (American Society for Testing and Materials). In each case, five specimens for each bio-composite were tested and the average value is tabulated. The experimental configuration is shown in Figure 5.

Figure 5.

Experimental setup.

Statistical analysis

RSM experimental design

The response surface methodology (RSM), firstly induced by Box and Wilson,^37,38 is a method for the accurate prediction of engineering system input–output relationships by taking a full consideration for parameter interaction. It has been widely applied in numerous manufacturing fields for the design, development and formulation of new products, as well as in the improvement of existing product designs. This procedure includes six steps. These are, (1) define the independent input variables and the desired output responses, (2) adopt an experimental design plan, (3) perform regression analysis with the quadratic model of RSM, (4) calculate the statistical analysis of variance (ANOVA) for the independent input variables in order to find parameters which significantly affect the response, (5) determine the situation of the quadratic model of RSM and decide whether the model of RSM needs screening variables or not and finally, (6) optimize, conduct confirmation experiment and verify the predicted performance characteristics.

The RSM offers several experimental designs depending on the number of design factors, such as Box–Behnken Design (BBD), Central Composite Design (CCD), etc. The BBD is selected to generate the design matrix since it needs fewer experiments when the number of factors is about 3–4.

A BBD was performed with four independent variables (X₁, type of fiber; X₂, chemical treatment; X₃, volume fraction of fiber; and X₄, treatment duration) at three levels. In addition, there are two qualitative variables (X₁, type of fiber and X₂, chemical treatment) and the other two are quantified (volume fraction of fiber and X₄, treatment duration). The independent variables, units, symbol code and levels were shown in Table 2. Total number of experiments carried out was 29, including eight axial, 16 factorial and five centre points (calculated based on equation (4)). Table 3 provides the detail of the 29 experimental conditions and the experimental values of output variables (Ultimate tensile strength, Young’s modulus, ultimate tensile strength and Young’s modulus).

{N = 2}^{n} {+ 2 n + N c = 2}^{4} + 2 \times 4 + 5 = 29 r u n s

(2)Where N is the total experimental runs, n is the number of variables and Nc replicate runs at the centre. For statistical calculation, each variable was coded at three levels: “–1”, “0” and “+1”, where “–1” is the lowest level, “0” is the central level and “+1” is the highest level. And each combination was repeated five times.

Table 2.

Levels of various independent variables at coded values of RSM experimental design.

N°	Independent variables	Symbol code, X_i	Units	Levels			Type
N°	Independent variables	Symbol code, X_i	Units	−1	0	+1	Type
1	Fibers type	X₁	–	Flax	Jute	Sisal	Qualitative
2	Types of chemical treatment	X₂	–	NaHCO₃	Raw	NaOH	Qualitative
3	Volume fraction of fiber	X₃	(%)	10	15	20	Quantitative
4	Treatment duration	X₄	hrs	4	12	24	Quantitative

Table 3.

Box-Behnken Design (BBD) with independent variables and response values.

N°	Coded values of independent variables				Actual values of independent variables				Response parameters
	Coded values of independent variables				Actual values of independent variables				Epoxy resin		Polyester resin
	X₁	X₂	X₃	X₄	Type of fibers	Types of chemical treatment	Volume fraction of fiber (wt.%)	Treatment duration (hrs)	Ultimate tensile strength σ_u (MPa)	Young’s modulus E (GPa)	Ultimate tensile strength σ_u (MPa)	Young’s modulus E (GPa)
1	0	0	1	1	Jute	Raw	20	24	59.4950	2.1294	54.6790	1.9618
2	0	0	0	0	Jute	Raw	15	12	36.9230	1.5264	30.9010	1.4993
3	0	1	0	1	Jute	NaOH	15	24	41.0820	1.5296	34.7470	1.5021
4	0	0	0	0	Jute	Raw	15	12	36.9230	1.5264	30.9010	1.4993
5	0	0	−1	1	Jute	Raw	10	24	25.1990	1.5610	22.6180	1.4621
6	−1	0	−1	0	Flax	Raw	10	12	17.0760	1.0054	13.7780	0.7593
7	0	−1	−1	0	Jute	NaHCO₃	10	12	28.3660	1.1244	25.9470	1.6540
8	0	1	1	0	Jute	NaOH	20	12	67.4130	2.0876	57.6010	1.5742
9	0	0	0	0	Jute	Raw	15	12	36.9230	1.5264	30.9010	1.4993
10	−1	0	0	−1	Flax	Raw	15	4	32.9420	1.3088	24.2340	1.1124
11	0	0	0	0	Jute	Raw	15	12	36.9230	1.5264	30.9010	1.4993
12	−1	0	0	1	Flax	Raw	15	24	32.9420	1.1124	24.2340	1.3088
13	1	0	1	0	Sisal	Raw	20	12	43.8400	1.2686	38.7200	1.7574
14	1	−1	0	0	Sisal	NaHCO₃	15	12	37.0240	1.4630	30.0830	1.6765
15	0	0	0	0	Jute	Raw	15	12	36.9230	1.5264	30.9010	1.4993
16	−1	−1	0	0	Flax	NaHCO₃	15	12	39.7170	1.5556	31.6490	1.4565
17	−1	0	1	0	Flax	Raw	20	12	59.8440	1.9096	56.6380	1.8817
18	1	0	0	−1	Sisal	Raw	15	4	34.7650	0.9320	23.5270	1.5393
19	1	0	0	1	Sisal	Raw	15	24	34.7650	0.9320	23.5270	1.5393
20	0	−1	0	−1	Jute	NaHCO₃	15	4	46.7150	1.7592	37.9030	1.7309
21	0	0	−1	−1	Jute	Raw	10	4	25.1990	1.5610	22.6180	1.4621
22	1	1	0	0	Sisal	NaOH	15	12	22.0310	1.3172	12.2240	1.3624
23	0	−1	1	0	Jute	NaHCO₃	20	12	59.0720	2.0088	62.4540	1.9810
24	−1	1	0	0	Flax	NaOH	15	12	36.4210	1.4885	28.5020	1.3450
25	0	1	−1	0	Jute	NaOH	10	12	45.4240	1.5518	16.9920	1.6726
26	0	−1	0	1	Jute	NaHCO₃	15	24	53.3400	2.0876	42.7940	1.7191
27	0	1	0	−1	Jute	NaOH	15	4	55.5760	1.5458	40.8370	1.6420
28	1	0	−1	0	Sisal	Raw	10	12	26.6330	0.9320	16.3380	1.3659
29	0	0	1	−1	Jute	Raw	20	4	59.4950	2.1294	54.6790	1.9618

A complete description of the process behavior requires a quadratic or higher order polynomial model. Hence, the full quadratic models were established by using the method of least squares, which included all interaction which is termed to calculate the predicted response. The quadratic model is usually sufficient for industrial applications. For n factors the full quadratic model is shown in equation (3):

Y = a_{0} + \sum_{i = 1}^{k} b_{i} X_{i} + \sum_{i, j}^{k} b_{i j} X_{i} X_{j} + \sum_{i = 1}^{k} b_{i i} X_{i}^{2}

(3)where Y is the response; X_i and Xj are the variables (i and j range from 1 to k); a₀ is the model intercept coefficient; b_i, b_ii and b_ij are the interaction coefficients of linear, quadratic and the second order terms, respectively; k is the number of independent variables (k = 4 in this study). The quality of the model was expressed by the adjusted-R² (R² adj), and predicted-R² (R² pred). The other important coefficient, R², which is called coefficient of determination in the resulting ANOVA tables, is defined as the ratio of the explained variation to the total variation and is a measure of the fit degree. When R² approaches to unity, it indicates a good correlation between the experimental and the predicted values. The regression analyses, graphical analyses, analyses of variance (ANOVA) and analyses of response surfaces were carried out using Design Expert software V8 (Stat-Ease). The significance of the independent parameters and their interactions and the adequacy of the developed model were estimated by analysis of variance (ANOVA).

Regression equations

In the empirical approach, prediction of ultimate tensile strength (σ_u) and Young’s modulus (E) were done based on the regression analysis of the experimental data. A statistical model gives relationship between response variables (ultimate tensile strength and Young’s modulus) and four independent parameters witch are; type of fibers (X₁), type of chemical treatment (X₂), volume fraction of fiber (X₃) and treatment duration (X₄) for both resins matrix used (epoxy and polyester). Based on the RSM method using the quadratic model of equation (2), the approximated quadratic equation is obtained in terms of coded values for both responses are presented in equations (4) to (7):

❖ Epoxy resin

\begin{array}{l} σ_{u} = 36.92 - 1.66 X_{1} + 0.3094 X_{2} + 15.11 X_{3} - 0.6557 X_{4} - 7.23 X_{1} \times X_{1} \\ + 7.05 X_{2} \times X_{2} + 5.15 X_{3} \times X_{3} + 3.21 X_{4} \times X_{4} \end{array}

(4)

\begin{array}{l} E = 1.53 - 0.128 X_{1} - 0.0398 X_{2} + 0.3162 X_{3} + 0.00965 X_{4} - 0.3725 X_{1} \times X_{1} \\ + 0.164 X_{2} \times X_{2} + 0.133 X_{3} \times X_{3} + 0.0478 X_{4} \times X_{4} \end{array}

(5)

❖ Polyester resin

\begin{array}{l} σ_{u} = 30.90 - 2.88 X_{1} - 3.33 X_{2} + 17.21 X_{3} - 0.0999 X_{4} - 8.24 X_{1} \times X_{1} \\ + 4.04 X_{2} \times X_{2} + 6.71 X_{3} \times X_{3} + 2.13 X_{4} \times X_{4} \end{array}

(6)

\begin{array}{l} E = 1.5 + 0.1148 X_{1} - 0.0933 X_{2} + 0.2285 X_{3} + 0.0037 X_{4} - 0.1711 X_{1} \times X_{1} \\ + 0.1054 X_{2} \times X_{2} + 0.1276 X_{3} \times X_{3} + 0.0586 X_{4} \times X_{4} \end{array}

(7)

Figure 6 presents a comparison between predicted and measured values of the ultimate tensile strength (σ_u) and Young’s modulus (E) of different bio-composites corresponding to different combinations of independent variables were presented in Table 3. The results of the comparison prove that predicted values of the ultimate tensile strength and Young’s modulus are very close to those experimentally recorded. Similarly, the Figure 7 shows the comparison between the values of the tensile proprieties (σ_u and E) of all fiber reinforced bio-composites corresponding to different combinations of independent variables. The fiber reinforced epoxy bio-composite provides higher values than the fiber reinforced polyester bio-composites in term ultimate tensile strength. For example; σ_u-epoxy ≈ 1.22 σ_u-polyester and E_u-epoxy ≈ 0.98 E_u-polyester.

Figure 6.

Comparison between measured and predicted values for; (a) ultimate tensile strength and (b) Young’s modulus of different bio-composites (natural fibers/epoxy and natural fibers/polyester).

Figure 7.

Comparison between different bio-composites (natural fibers/epoxy and natural fibers/polyester) for; (a) ultimate tensile strength; (b) Young’s modulus.

Analysis of variance ANOVA

The ANOVA of the data with the ultimate tensile strength (σ_u) and Young’s modulus (E), with the objective of analysing the influence of type of fibers (X₁), types of chemical treatment (X₂), volume fraction of fiber (X₃) and treatment duration (X₄) on the total variance of the results were carried out. The table of ANOVA shows the degrees of freedom (DF), sum of squares (SC), mean squares (MS), F-values (F) and probability (P). The last column of table shows the factor contribution percentage (Cont. %) on the total variation for each factor and different products. The statistical significance of each coefficient was checked by p-values and F-values. A low p-value (≤0.05) indicates statistical significance for the source on the corresponding response (i.e., α = 0.05, or 95% confidence level), this indicates that the obtained models are considered to be statistically significant, which is desirable; as it demonstrates that the terms in the model have a significant effect on the response, and the higher F-values for each coefficient suggest more significance of that term in the model.^39,40

As can be seen from Table 4, the regression model for ultimate tensile strength was found to be highly significant from the Fisher’s test which had a high F-values (12.27 and 35.73) with very low probability (both p < .0001 and both p-values <0.05) according to, epoxy and polyester resins, respectively. Judging by the F-values of the items in the regression models, the order in which the independent variables influenced the ultimate tensile strength degradation efficiency was: the volume fraction of fiber (X₃) which is the most important factor affecting the tensile strength. Its contribution is (74.33% for σ_epoxy and 82.46% for σ_polyester). Similar results were reported by Ben Brahim,⁴¹ when they study the effect of volume fraction of fibers on the tensile properties (longitudinal modulus and the longitudinal stress) of unidirectional alfa-polyester composites. The type of fibers (X₁), types of chemical treatment (X₂) and treatment duration (X₄) have a very weak significance effect on the ultimate tensile strength when compared volume fraction of fiber. The quadratic coefficients (X₁×X₁, X₂×X₂ and X₃×X₃) were found to be significant (P ˂ 0.05). The increasing of volume fraction of fiber (X₃×X₃) had a negative effect on the ultimate tensile strength. Our finding is against the report by Idicula⁴²; i.e. they have found that the quality of the bio-composites has been increased with the boost of volume fraction of the sisal and banana fibers. The permissible fiber volume fraction was mentioned as 40% beyond that the proper stress transfer from fibers to matrix was not occurred due to fiber agglomeration.

Table 4.

Analysis of variance for ultimate tensile strength (σ_u).

Source	SS	DF	MS	F-value	p-value	Cont. %	Remarks
(a) Epoxy resin
Model	3822.65	8	477.83	12.27	<0.0001		Significant
X₁	32.95	1	32.95	0.8462	0.3686	0.90	Insignificant
X₂	01.15	1	01.15	0.0295	0.8653	0.03	‒
X₃	2737.99	1	2737.99	70.32	<0.0001	74.46	Significant
X₄	05.16	1	05.16	0.1325	0.7197	0.14	Insignificant
X₁×X₁	338.8	1	338.80	8.70	0.0079	9.21	Significant
X₂×X₂	321.98	1	321.98	8.27	0.0093	8.76	‒
X₃×X₃	172.36	1	172.36	4.43	0.0482	4.69	‒
X₄×X₄	66.93	1	66.93	1.72	0.2047	1.82	Insignificant
Error	778.75	20	38.94
Total	4601.4	28				100
(b) Polyester resin
Model	4835.41	8	604.43	35.73	<0.0001		Significant
X₁	99.86	1	99.86	5.90	0.0247	2.15	‒
X₂	132.85	1	132.85	7.85	0.0110	2.85	‒
X₃	3552.83	1	3552.83	210	<0.0001	76.33	‒
X₄	0.1198	1	0.1198	0.0071	0.9338	0.01	Insignificant
X₁×X₁	440.47	1	440.47	26.04	<0.0001	9.47	Significant
X₂×X₂	106.10	1	106.10	6.27	0.0210	2.28	‒
X₃×X₃	292.09	1	292.09	17.26	0.0005	6.28	‒
X₄×X₄	29.35	1	29.35	1.74	0.2027	0.63	Insignificant
Error	338.36	20	16.92
Total	5173.77	28				100

Moreover, the determination coefficients of the models (R²) have very high values (0.8308 and 0.9346), which mean that 83.08 and 93.46% of the total variation is explained by this quadratic regression model. Besides, the values of the adjusted determination coefficient (R² Adjusted) are 0.7631 and 0.9084, for epoxy and polyester matrix, respectively; indicating that the predicted and experimental ultimate tensile strength efficiencies have a high degree of accordance.

Similarly, results from ANOVA (Table 5) for the quadratic model showed that the polynomial models were highly statistically significant, as suggested by the high model F-values (10.15 for E_epoxy and 7.37 for E_polyester) and low p-values (both <0.0001 for E_epoxy and E_polyester, both p-values <0.05). The F and p values are used to check the significance of each coefficient. The lower p-value and higher F-value indicated the more significance of corresponding coefficient. The high values of determination coefficients (R²_epoxy = 0.8024 and R²_polyester = 0.7467), and adjusted determination coefficients (adj-R_epoxy = 0.7233 and adj-R_epoxy = 0.6454) showed a high degree of correlation between the experimental and the predicted values of Young’s modulus. In addition, according to Tables 5 and it can be apparently the volume fraction of fiber (X₃) which is the most important factor affecting on Young’s modulus (E). Its contribution is (Cont. = 45.78 % and Cont. = 48.99 %) for epoxy and polyester resins, respectively. The next factor influencing E is the type of fibers (X₁) with (Cont. = 7.50 % and Cont. = 12.35 %), there are lots of work that has been done to study the effect of fiber loading on the mechanical properties.^43,44 Whereas, the chemical treatment (X₂) with (Cont. = 0.72 % and Cont. = 8.17 %) was found to be less significant on Young’s modulus of epoxy and polyester resins, respectively. Similarly, the treatment duration (X₄) has a very weak effect on the Young’s modulus due to higher p-value (p > .05). Its contribution is (Cont. = 0.04 % and Cont. = 0.02 %) for epoxy and polyester resins, respectively.

Table 5.

Analysis of variance for Young’s modulus (E).

Source	SS	DF	MS	F-value	p-value	Cont. %	Remarks
(a) Epoxy resin
Model	2.90	8	0.3629	10.15	<0.0001		Significant
X₁	0.1965	1	0.1965	5.49	0.0295	07.50	‒
X₂	0.019	1	0.019	0.5327	0.4739	0.72	Insignificant
X₃	1.20	1	1.2	33.61	<0.0001	45.78	Significant
X₄	0.0011	1	0.0011	0.0313	0.8615	0.04	Insignificant
X₁×X₁	0.9005	1	0.9005	25.18	<0.0001	34.35	Significant
X₂×X₂	0.1748	1	0.1748	4.89	0.0388	06.67	‒
X₃×X₃	0.1146	1	0.1146	3.2	0.0886	04.37	Insignificant
X₄×X₄	0.0148	1	0.0148	0.4144	0.5271	0.56	‒
Error	0.7151	20	0.0358
Total	3.62	28				100
(b) Polyester resin
Model	1.36	8	0.1695	7.37	0.0001		Significant
X₁	0.158	1	0.158	6.87	0.0163	12.35	‒
X₂	0.1045	1	0.1045	4.54	0.0456	8.17	‒
X₃	0.6265	1	0.6265	27.25	<0.0001	48.99	‒
X₄	0.0002	1	0.0002	0.0072	0.933	0.02	Insignificant
X₁×X₁	0.1899	1	0.1899	8.26	0.0094	14.85	Significant
X₂×X₂	0.072	1	0.072	3.13	0.092	05.63	Insignificant
X₃×X₃	0.1056	1	0.1056	4.59	0.0446	08.26	Significant
X₄×X₄	0.0222	1	0.0222	0.9672	0.3371	01.74	Insignificant
Error	0.4599	20	0.023
Total	1.82	28				100

Perturbation plots

The main effects of the single factor (X₁, X₂, X₃ and X₄) and the perturbation plots for both response parameters (σ_u and E) are illustrated in Figure 8. They confirm the ANOVA results demonstrated in Tables 4 and 5 The x-axis in the graphs is the low and high level of the design factor and y-axis is the mean value of the response parameter at a specific design factor level. Figure 8(a) illustrates the perturbation plot of σ_u for both resins. According to this plot, we understand that the forth factors X₁, X₂, X₃ and X₄ have a positive quadratic influence on σ_u and E. Therefore, the volume fraction (X₃) seemed to have a major role for improving the mechanical properties (σ_u and E) of natural bio-composites while the treatment duration (X₄) played the minor. In addition, the factors (X₁, X₂ and X₃) at different levels have different influence on the σ_u. The Same way, Figure 8(b) represents the perturbation plot for E. From this graph, we can figure out that X₁, X₂ and X₃ have significant impacts on the E, while X₄ has less effect. At the same time, all the four processing parameters have positive effects on the E.

Figure 8.

Main affects graphs for σ_u and E versus X₁, X₂, X₃ and X₄ of the both resin matrices (epoxy and polyester).

Effect of operating parameters on grafting

In order to present the relationship between independent variables (type of fibers (X₁), types of chemical treatment (X₂) and volume fraction of fiber (X₃)) on response parameters (ultimate tensile strength (σ_u) and Young’s modulus E), the response surface plots of the models were generated and illustrated on the three-dimensional space by varying two variables within the experimental range for both resins matrix tested (epoxy and polyester), while the treatment duration (X₄) is kept at the middle level (1 h) and when using three different natural fibers, namely and coded; flax fiber (−1), jute fiber (0) and sisal fiber (+1). From Figures 9 and 10, we could observe the remarkable increases of both response parameters (σ_u and E) with increases in volume fraction of fiber and chemical treatment in the bio-composites arranged in specific order which is jute, sisal and flax. In addition, these two variables exhibited significant positive quadratic effects in response of response parameters (Equation (4) and (5). Moreover, the surface modifications could remove surface impurities and increase surface roughness. These modifications increase to bonding of the fiber with the resins matrix there by improving the fiber-matrix interaction, subsequently, significantly increased the ultimate tensile strength and Young’s modulus of the composites. Several authors,^45–48 have focussed the studies on the treatment of fibers to improve the bonding with resin matrix. For example, Zou⁴⁹ studied the effects of alkali and silane surface treatments on sisal fiber properties and they observed that the surface treatments facilitated good adhesion between fibers and thermoplastic matrix resulting in composites with improved mechanical properties.

Figure 9.

Comparison of response surface for ultimate tensile strength vs X₁, X₂, and X₃ at the middle level of X₄; (a) epoxy matrix and (b) polyester matrix.

Figure 10.

Comparison of response surface for Young’s modulus vs X₁, X₂, and X₃ at the middle level of X₄; (a) epoxy matrix and (b) polyester matrix.

Generally, the plots indicate that the highest ultimate tensile strength and Young’s modulus can be achieved at the volume fraction of fiber is higher (level +1) with NaHCO₃ chemical treatment (level −1), when using jute fiber (level 0) and when reinforcing bio-composites with epoxy matrix. Also, for all types of fibers a quadratic increase in the response parameters with increasing the volume fraction of fiber were observed. This is due to the high fibers tensile modulus compared to resins matrix.

Multiple response optimizations

In this section, the statistical method of RSM was used to calculate desirability for the optimization analysis. This approach is a multi-criteria methodology often applied when various responses have to be considered at the same time and it is necessary to find optimal comprises between the total numbers of responses taken into account. The Derringer function or desirability (D) function is the most important and most currently used multi-criteria methodology in the optimization of analytical procedures.⁵⁰ The global D value was analyzed basing on individual desirability’s. Statistical analyses were performed operating the Design Expert software V8 (Stat-Ease). The constraints used during the optimization process are summarized in Table 6. The best optimal values of independent variables and responses are reported in Table 7 in order of decreasing desirability level, the optimal process factors were found to be as follows: the type of fibers (X₁) of [−0.28 and −0.33], the type of chemical treatment (X₂) of −1, the volume fraction of fiber (X₃) of 1 and the treatment duration (X₄) of [−0.97 and −1] for epoxy bio-composites. Similarly, when using the polyester matrix; the type of fibers (X₁) of −0.26, the type of chemical treatment (X₂) of −1, the volume fraction of fiber (X₃) of ∼1 and the treatment duration (X₄) of [−0.94 and −0.93].

Table 6.

Constraints for optimization of independent variables.

Conditions	Goal	Lower limit		Upper limit
Conditions	Goal	Epoxy resin	Polyester resin	Epoxy resin	Polyester resin
(X₁): Type of fibers	In range	−1		+1
(X₂): Types of chemical treatment	In range	−1		+1
(X₃): Volume fraction of fiber (%)	In range	−1		+1
(X₄): Treatment duration (hrs)	In range	−1		+1
Ultimate tensile strength σ (MPa)	Maximize	17.076	12.224	67.4130	62.4540
Young’s modulus E (GPa)	Maximize	0.9320	0.7593	02.12940	01.9810

Table 7.

Response optimization for ultimate tensile strength and Young’s modulus.

Test N°	Coded factors				Performance measures		Desirability D	Remarks
Test N°	X₁	X₂	X₃	X₄	Ultimate tensile strength σ (MPa)	Young’s modulus E (GPa)	Desirability D	Remarks
Epoxy resin
1	−0.28	−1.00	1.00	−0.97	67.4484	2.2211	1	Selected
2	−0.33	−0.99	1.00	−1.00	67.4236	2.2165	1
3	−0.47	−1.00	1.00	−1.00	66.9613	2.1954	0.9966
Polyester resin
1	−0.26	−0.97	0.99	−0.94	63.8168	2.0487	1	Selected
2	−0.26	−0.95	0.99	−0.93	63.5924	2.0431	1
3	−0.40	−0.54	1.00	−1.00	59.8618	1.9188	0.9488

Validation of experimental results

Creation of the hybrid bio-composites

Once the optimal level of the process parameters is selected, the final step is to predict and verify the improvement of the performance characteristics (ultimate tensile strength and Young’s modulus) using the optimal levels of the process parameters presented in terms of coded factors in section multiple response optimizationsin. To make the confirmation tests, we converted the coded values presented in Table 8 to the actual values. The rule of mixture fiber has been presented in Figure 11.

Table 8.

Optimal levels of factors in actual terms.

N°	Coded factors				Actual factors
N°	X₁	X₂	X₃	X₄	Type of fibers content (%)	Type of chemical treatment	Volume fraction of fiber (%)	Treatment duration (hrs)
Epoxy resin
1	−0.28	−1	+1	−0.97	72% of jute and 28% of flax	NaHCO₃	20	4 h14
2	−0.33	−1	+1	−1	67% of jute and 33% of flax	NaHCO₃	20	4 h00
Polyester resin
1	−0.26	−1	+0.99	−0.94	74% of jute and 26% of flax	NaHCO₃	19.80	4 h28
2	−0.26	−1	+0.99	−0.93	74% of jute and 26% of flax	NaHCO₃	19.80	4 h33

Figure 11.

Rule of hybrid mixture.

Hybrid bio-composites manufacturing methods

In this study the hybrid bio-composites are fabricated by hand-lay up method. Bio-composites were made using a wood mould measuring: 300 × 150 × 5 mm length, width and depth, respectively. Four beadings a glass plate were used to maintain a 5 mm thickness all around the mould plates. Initially, the moulds are cleaned, dried and silicon spray was used as releasing agent. Secondly, untreated jute, and flax fibers were, weighed, bagged and formulated according to the various fiber contents indicated in Table 8. In this section, fibers were treated with sodium bicarbonate NaHCO₃, with various times 4 to 4.33 h (see Table 9). Then, the fibers were mixed with resin matrices (epoxy or polyester) for the fabrication of bio-composite. Then, uniform pressure of 5 Pa was applied over the mould plates (purpose of this is to maintain uniform thickness and to avoid void formation during curing) for 1 h at room temperature curing. Finally, the bio-composites were cut and shaped in rectangular form (250 × 25 × 5 mm) according to ASTM standard by using a diamond saw blade. Four identical samples were prepared for each volume fraction of fibers and all the specimens were tested at a strain rate of 2 mm/min using an electronic tensometer Table 10.

Table 9.

Designations and composition of hybrid bio-composites.

n°	Code	Composites	Volume fraction		Resin content (wt. %)	Treatment duration (hrs)
n°	Code	Composites	Jute fiber content (wt. %)	Flax fiber content (wt. %)	Resin content (wt. %)	Treatment duration (hrs)
Epoxy resin
1	HCE1	Hybrid bio-composite	14.40	05.60	80	4 h14
2	HCE2	Hybrid bio-composite	13.40	06.60	80	4 h00
Polyester resin
1	HCP1	Hybrid bio-composite	14.65	05.15	80.20	4 h28
2	HCP2	Hybrid bio-composite	14.65	05.15	80.20	4 h33

Table 10.

Result of tensile test of different bio-composites.

n°	Sample code	Experimental		Error (%)
n°	Sample code	Ultimate tensile strength σ_u (MPa)	Young’s modulus E (GPa)	σ_u (MPa)	E (GPa)
a) Epoxy resin
1	HCE1	64.7100	2.1900	4.06	4.10
2	HCE2	60.2600	2.1800	10.62	1.65
b) Polyester resin
1	HCP1	62.6200	1.8122	1.88	11.54
2	HCP2	62.6800	1.9400	1.43	5.05

Confirmation test results

The four different hybrid bio-composite specimens: HCE1, HCE2, HCP1 and HCP2 (see Table 8) are tested in the universal testing machine to find the tensile properties. Each test was repeated four times and the results are displayed as strength against strain. Also, Figure 12(a)–(d) shows the tensile testing data of various hybrid bio-composites with different fibers content reinforced with epoxy or polyester resins are linear and follows Hooke’s law. Then, the Young’s modulus is calculated by taking the corresponding values of stress and strain from the linear portion of the graph before the first damage zone. The averages of tensile properties (ultimate tensile strength and Young’s modulus) are summarized in the Table 1.

Figure 12.

Stress vs strain curve for tensile test of various hybrid bio-composites at highest volume fraction of fiber (20%).

The average values of the ultimate tensile strength and Young’s modulus of different bio-composite formulations are plotted in Figure 13 for better comparison. It is observed that the hybrid bio-composite HCE1 had the highest tensile properties (ultimate tensile strength and Young’s modulus) especially when reinforced with epoxy. The lowest tensile properties were produced by jute bio-composite when reinforced with epoxy. The modulus value for the hybrid bio-composite HCP2 fell within these two values.

Figure 13.

Tensile properties of various bio-composites for both resin matrices: (a) ultimate tensile strength and (b) Young’s modulus.

Conclusion

This paper may help to advance the use of Response Surface Methodology RMS technique, especially with BBD, for the optimization of simultaneous variables, for hybrid polymer bio-composites in the future. Several major findings are summarized in this study and the accompanying conclusions made, as below:

1) The ANOVA shows that:

(a) The volume fraction of fiber (concentration of the fiber in the bio-composite) plays a significant role on the mechanical properties (ultimate tensile strength σ_u and Young’s modulus E) of bio-composites with contributions between (74.46 and 45.78) % for epoxy matrix and (76.33 and 48.99) % for polyester matrix, respectively. The next largest factor influencing on σ_u and E is the type of fibers with contributions between [(Cont._epoxy-resin ≈ 0.90 and Cont. _{polyester-resin} ≈ 2.15) % and (Cont._epoxy-resin ≈ 07.50 and Cont. _{polyester-resin} ≈ 12.35)] %, respectively. It is followed by the chemical treatment in third position. The treatment of duration comes in the fourth position.

(b) Additionally, this study shows that the chemical treatment at lower treatment duration has a positive effect on the tensile properties of bio-composites by improving the adhesion between natural fiber and polymer matrix by eliminating pectin and wax from natural fiber.

(c) The mean ultimate tensile strength and the mean Young’s modulus of jute fiber bio-composite at in range of volume fraction of fiber in the present study is much higher than those of flax and sisal bio-composites is this for both resins tested. It is also concluded that the mechanical properties of bio-composite reinforced with an epoxy matrix provides higher properties than the bio-composite reinforced with polyester matrix.

2) From multi-objective optimization:

(a) The mathematical models elaborated for ultimate tensile strength σu and Young’s modulus E are very reliable, and they represent an important industrial interest, since they help to make predictions within the range of the actual experimentation.

(b) The optimization process is done by maximizing both ultimate tensile strength and Young’s modulus. It depends on the following factor combinations: the type of fibers (X1) of −0.28 and −0.33], the chemical treatment (X2) of −1, the volume fraction of fiber (X3) of 1 and the treatment duration (X4) of [−0.97 and −1] for epoxy matrix. Similarly, when used the polyester matrix: the type of fibers (X1) of −0.26, the chemical treatment (X2) of −1, the volume fraction of fiber (X3) of ∼1 and the treatment duration (X4) of [−0.94 and −0.93].

3) The mechanical characteristics (tensile strength and the Young’s modulus) of hybrid materials are better than the jute, flax and pure sisal characteristics by (8.96%, 3.86%, 9.90% and 2.92%, 10.04%, 21.46%) respectively for epoxy resin and by (0.27%, 5.68%, 14.9% and 2.02%, 6.03%, 5.36%) respectively for polyester resin.

4) In addition, the hybrid bio-composite specimens HCE1 exhibits higher tensile proprieties and can withstand the strength up to 64.71 MPa; hence provide higher Young’s modulus of 2.19 GPa.

5) This study provides evidence that hybridization of natural/natural fibers improved the tensile properties of bio-composites when compared to individual fibers.

Footnotes

Acknowledgments

This work was carried out at the Higher National School of Technology, Alger, Algeria as part of a PRFU research project, Code: A11N01ES161220220004. The authors thank the Directorate General of Scientific Research and Technological Development (DGRSDT) of (MESRS) for the financial support.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Laouici Hamdi

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Tensile mechanical performance of natural/natural fiber reinforced hybrid bio-composite materials – A statistical approach

Abstract

Keywords

Introduction

Materials and methods

Materials

Jute fiber

Sisal fiber

Flax fiber

Fiber preparation methods

Mechanical tensile properties of individual fibers

Treatment with NaOH

Treatment with NaHCO3

Scanning electron microscopy studies

Mechanical properties of bio-composite

Statistical analysis

RSM experimental design

Regression equations

Analysis of variance ANOVA

Perturbation plots

Effect of operating parameters on grafting

Multiple response optimizations

Validation of experimental results

Creation of the hybrid bio-composites

Hybrid bio-composites manufacturing methods

Confirmation test results

Conclusion

Footnotes

Acknowledgments

Declaration of conflicting interests

Funding

ORCID iD

Appendix

References

Treatment with NaHCO₃