Optimization of Characteristics Polymer Composite Reinforced Kenaf and Jute Fiber Using Taguchi-Response Surface Methodology Approach

ABSTRACT

The use of composite materials with natural fiber reinforcement (CMNFR) has experienced rapid development in the automotive industry to apply synthetic materials that are expensive and not environmentally friendly. Biocomposite is a composite consisting of a polymer matrix and natural fiber reinforcement. Natural fiber materials are used as a substitute for conventional non-renewable reinforcing materials. This study uses the vacuum-assisted resin infusion (VARI) process to fabricate composites using woven kenaf and jute from natural fiber. The Taguchi experimental design method with L₂₇³ as an orthogonal array matrix was used in this research to obtain the optimum CMNFR fabrication parameters. Response Surface Methodology is used to obtain the optimal response value based on microstructural analysis. The factors used were NaOH treatment (4, 6, 8 wt%), water absorption test (6, 12, 24 hours), and fiber type (KF, JF, KF-JF). The study’s results found a combination of optimization of CMNFR manufacturing parameters using RSM, namely, A₁B₁C₂ with a mean material strength of 0.241 J/mm² (impact strength) and a combination of A₁B₃C₁ parameters with a mean material strength of 94.8650 MPa (flexural strength). It was also found that the presence of contaminants and voids harmed the mechanical properties.

摘要

天然纤维增强复合材料（CMNFR）的使用在汽车行业经历了快速发展，以应用昂贵且不环保的合成材料. 生物复合材料是一种由聚合物基质和天然纤维增强物组成的复合材料. 天然纤维材料被用作传统的不可再生增强材料的替代品. 本研究采用真空辅助树脂注入（VARI）工艺，用天然纤维编织红麻和黄麻制备复合材料. 本研究采用田口实验设计方法，以L2733为正交阵列矩阵，获得了最佳的CMNFR制备参数. 响应面方法用于在微观结构分析的基础上获得最佳响应值. 使用的因素是NaOH处理（4、6、8 wt%）和吸水试验（6、12、24小时）、纤维类型（KF、JF、KF-JF）. 该研究的结果发现了使用RSM的优化CMNFR制造参数的组合，即材料强度平均值为0.241 J/mm2（冲击强度）的A1B1C2和材料强度平均为94.8650 MPa（弯曲强度）的AlB3C1参数的组合. 还发现，污染物和空隙的存在损害了机械财产.

KEYWORDS:

• Composite material
• automotive industry
• natural fiber material
• Taguchi method
• responsesurface-methodology

关键词:

• 复合材料
• 汽车工业
• 天然纤维材料
• 田口方法
• 响应面方法

Introduction

The automotive industry’s development is growing rapidly, so many researchers are developing their research in the automotive field related to the development of composite materials. The use of synthetic materials in the production process on a large scale is detrimental to the environment and requires high production costs (Alzebdeh, Nassar, and Arunachalam 2019). The negative environmental and economic impacts of using synthetic fibers encourage the need for alternative materials with lower production costs, environmental friendliness, and good mechanical properties.

Natural fiber is an environmentally friendly material used as a reinforcement to make biocomposites in various industrial applications (Yashas Gowda et al. 2018). The development of natural fiber-reinforced composite materials as an alternative to synthetic materials provides the advantages of lower production costs, safety, environmental friendliness, and good mechanical properties (Alzebdeh, Nassar, and Arunachalam 2019). According to the reference, there are shortcomings in natural fibers. Namely, they cannot be applied to the requirements of high material strength and water absorption and cannot withstand high temperatures (Sanjay et al. 2019).

One source of natural fiber is kenaf fiber (KF) (Sathyaseelan, Sellamuthu, and Palanimuthu 2021). Kenaf or Hibiscus cannabinus is an annual herbaceous plant from the Malvaceae family, which is very important for making ropes, sacks, and carpets because of its fiber content. From this point of view, KF can be chosen as fiber reinforcement because it is cheap, has high availability, has less production time, is easy to process, and is highly preferred by users for biocomposite fabrication (Prakash and Viswanthan 2019). KF has been the most widely used natural fiber as thermoset and thermoplastic reinforcement for more than 20 years, offering the most sustainable option to reduce the use of synthetic fibers (Saba et al. 2016). Likewise, hemp fiber appears to be cheaper and easier to obtain than natural fibers as compared to allowing a high fill rate, thereby saving costs in composite manufacturing (Dilfi et al. 2018). Jute fiber (JF) is one of the most do. Previous research has been conducted on kenaf fiber-reinforced composites (Khan et al. 2020, 2020; Prakash and Viswanthan 2019). There is a study on the mechanical characterization of hybrid epoxy-based composites reinforced with vetiver fiber and kenaf fiber. Five types of composite laminates have been developed using the hand lay-up process, varying the percentage of vetiver and kenaf fibers. The results showed that the properties of the epoxy composite were improved by hybridization with vetiver and kenaf fibers. The improved mechanical properties of the fiber-reinforced composites were found in the percentage composition of the kenaf fiber (Prakash and Viswanthan 2019). Then, there is research on fabricating and characterizing Echinoidea spike particles and kenaf natural fiber-reinforced Azadirachta indica blended epoxy multi-hybrid biocomposite. The results showed that the addition of surface-treated sea urchin particles and kenaf fibers improved the mechanical properties of the composite. Similarly, thermal effects exposed that the addition of sea urchin bioceramic filler increased the thermal stability of neem epoxy biocomposite. Scanning electron microscopy (SEM) showed uniform dispersion of sea urchin filler and improved adhesion of kenaf fiber with epoxy (Prakash and Viswanthan 2019). Research on the effect of cellulose nanofibers and nano clays on the mechanical, morphological, thermal and dynamic mechanical performance of kenaf/epoxy composites is presented. The results obtained showed that the integration of rigid composite natural fiber as fillers improves the mechanical and thermal properties, storage and loss modulus, while a considerable reduction in Tan δ was achieved compared to kenaf/epoxy composites and the results obtained showed that OMMT and composites natural fiber-based kenaf/epoxy composites can be an efficient alternative for construction applications (Khan et al. 2020).

Jute seems less expensive and easily available compared to the opposite natural fibers, allowing high filling levels to save composite manufacturing costs (Dilfi et al. 2018). Jute fiber (JF) is one of the most main dominating bast fibers that are used as reinforcing materials for CMNFR (Sarker et al. 2019).

In previous studies (Devireddy and Biswas 2017; Dilfi et al. 2018; Sarker et al. 2019), there has been research on the effect of surface modification of jute fiber on the mechanical properties and durability of jute fiber reinforced epoxy composites. This study investigated the influence of various surface modifications on the properties and durability of jute fiber reinforced epoxy composites. The jute fibers have been surface modified to improve their compatibility with the hydrophobic epoxy matrix. The surface modifications examined include alkali treatment, silane treatment, and the combined effect of alkali and silane treatment. The results showed that the combined improvements provided better mechanical and thermal properties of the composite compared to the untreated composite due to the strong interfacial adhesion of the fiber matrix. The water absorption of the chemically treated fiber composite was lower than that of the untreated fiber composite. The mechanical and thermomechanical properties of composites were significantly reduced after exposure to moisture. This may be due to fiber degradation and delamination at the interface when the composite sample is immersed in water (Dilfi et al. 2018). Then, the ultrahigh performance of nanoengineered graphene-based natural jute fiber composites is researched. The results obtained showed that the nanoengineered graphene-based natural jute fiber acts as a new fiber structure (NFA) that significantly improves mechanical properties and performance (Sarker et al. 2019). Then, the physical and mechanical behavior of unidirectional banana/jute fiber-reinforced epoxy-based hybrid composites is researched. In this study, the present investigation deals with the fabrication and mechanical properties of unidirectional banana/jute hybrid fiber-reinforced composites and compares them with single natural fiber-reinforced composites. The results achieved with hybrid composites have promising results compared to individual fiber composites (Devireddy and Biswas 2017).

The optimization of CMNFR was successfully carried out by previous researchers (Devireddy and Biswas 2017; Dilfi et al. 2018; Khan et al. 2020, 2020; Prakash and Viswanthan 2019; Sarker et al. 2019), but all of them focused on the analysis of mechanical strength only. This has an impact on not being optimal in determining the strength of the CMNFR material. A good optimization technique is needed to obtain optimal CMNFR parameters through an experimental process. This study will comprehensively explain the application of the experimental method with the Taguchi-Response Surface Methodology (TM-RSM) hybrid approach. Research on experimental methods with the TM-RSM hybrid approach in the field of natural fiber-reinforced composites is still relatively new among researchers in Indonesia. In this study, RSM and Taguchi, in a practical sense, should be an alternative, and even complement each other’s shortcomings and when these two methods are integrated, the optimization concept in RSM will coexist with the Robust concept in Taguchi. This study can be an additional reference on optimizing CMNFR manufacturing with the TM-RSM hybrid approach for researchers, practitioners, and companies in the automotive industry.

Material and methodology

Materials

As shown in Figure 1, the kenaf woven fiber is produced by natural fiber craftsmen from Wirobrajan, Special Region of Yogyakarta, Indonesia. The jute woven fiber, as shown in Figure 2, used is produced by natural fiber craftsmen from Surakarta, Central Java, Indonesia.

Figure 1. Kenaf fiber.

Figure 2. Jute fiber.

Composites consist of two main components, namely, reinforced and matrix. In this study, the matrix is Bisphenol-A Epoxy Resin. The characteristics of Bisphenol A Epoxy Resin are presented in Table 1.

Table 1. Bisphenol-A epoxy resin property.

Properties	Unit	Value
Viscosity	MPa	13.000 ± 2.000
Epoxy number	%	22.7 ± 0.6
Epoxy equivalent	g/equiv	189 ± 5
Epoxy value	Equiv/100 g	0.53 ± 0.01
Total chlorine content	%	<0.2
Hydrolyzable chlorine content	%	<0.05
Color according to the Gardner scale	-	<1
Density at 25°C	g/cm^3	1.17 ± 0.01
Refractive index at 25°C	-	1.572 ± 0.003
Volatile content at 3 h, 140°C	%	<0.2
Vapor pressure at 80°C	mbar	<0.1
Flashpoint, according to DIN 51,584	°C	>250

Alkaline Treatment

When natural fibers are exposed to the atmosphere, they absorb moisture, thereby weakening the mechanical properties of the fibers. This moisture needs to be removed or reduced by alkaline treatment methods (Alzebdeh, Nassar, and Arunachalam 2019). Natural fibers were cleaned with clean water and soaked in NaOH solution for 2 hours at room temperature (Alzebdeh, Nassar, and Arunachalam 2019). Alkaline treatment has an effect on the fiber such as removing the waxy layer on the fiber, lignin, oil, and dirt; increasing the surface roughness of the fiber; and resulting in better mechanical properties (Adeniyi et al. 2019). Three levels of NaOH were used for the test samples in this research, namely, 4, 6, and 8 (wt%) concentrations for 4 hours, following methods adapted from Hamidon et al. (2019). It is not recommended to treat kenaf fiber with a NaOH solution greater than 10%, where this concentration can damage the texture of the fiber (Mahjoub et al. 2014). Finally, the fiber was washed with distilled water to remove any NaOH particles until neutral and then dried in air for 24 hours (Guo, Sun, and Satyavolu 2019).

Post-curing

After the manufacturing process, the composite plate received post-curing treatment. The post-curing process removes the water contained in the fibers. Post-curing is also performed to improve the modulus and strength of both polymers and composites and reduce residual stress. In this study, the temperature for post-curing treatment is 80°C, 100°C, and 120°C for 2 hours, determining the temperature value used as a parameter in this study was adopted from previous research by Devireddy and Biswas (2017).

Determination of Taguchi method

The Taguchi experimental design method with the L₂₇3³ as an orthogonal array matrix (which shows that the Taguchi Model consists of 27 experiments, three factors with three levels in each factor) was used in this research using the Minitab 19 software. Taguchi’s experimental design was used to reduce the number of trials, reduce the cost, and achieve the optimum result in a shorter time (Terzioğlu 2020). The Taguchi method provides a systematic and efficient methodology for optimizing the design of system parameters with much less effect than would be required for most optimization techniques (Montgomery and Runger 2014). The Taguchi method also used the SN ratio to analyze the effects of contributing factors on the responses. There are three SN ratio characteristics; the “lowest is the best,” the “highest the best,” and the “highest nominal is the best” in the process parameter optimization (Anggoro et al. 2019),

(1) $SNratio=-10log\frac{1}{n}(y_1^2+y_2^2+\dots +y_n^2).$(1)

There are three control factors and three levels of each factor that will affect the results of natural fiber-reinforced composite manufacturing, the first factor is the NaOH concentration (A), the second factor is the post-curing temperature (B) and the third factor is the fiber type (C) as shown in Table 2.

Table 2. CMNFR fabrication parameters.

Control factor	Level 1	Level 2	Level 3
A	4%	6%	8%
B	80°C	100°C	120°C
C	JF	KF	KF-JF

The three factors and the three levels of each factor will be processed using the Minitab 19 software and produce the experimental design shown in Table 3.

Table 3. Design matrix of orthogonal array L₂₇³ for the experimental runs.

Trial number	A	B	C
1	1	1	2
2	1	1	2
3	1	1	2
4	1	2	1
5	1	2	1
6	1	2	1
7	1	3	3
8	1	3	3
9	1	3	3
10	2	1	1
11	2	1	1
12	2	1	1
13	2	2	3
14	2	2	3
15	2	2	3
16	2	3	2
17	2	3	2
18	2	3	2
19	3	1	3
20	3	1	3
21	3	1	3
22	3	2	2
23	3	2	2
24	3	2	2
25	3	3	1
26	3	3	1
27	3	3	1

In the Taguchi method, the orthogonal array matrix is designed to provide the number of trials. Tables 2 and 3 show the experimental design by selecting the L₂₇3³ orthogonal array. In this study, the characteristics of the SN ratio used are “larger is better” as the optimization process parameter.

Response surface methodology

RSM is the combination of statistics and mathematical procedures that utilize system modeling and problem analysis to create a response of interest. This response is affected by several variables and the target value (Campana et al. 2018). RSM can analyze a set of designed experimental parameters to get an optimal response (Alzebdeh, Nassar, and Arunachalam 2019). This research analyzed the effect of CMNFR fabrication parameters on bending and tensile strength using RSM. In general, second-order polynomial functions are commonly used in various studies. The second-order RSM models are

(2) $y={\beta }_0+{\sum }_{i=1}^k{\beta }_i{x}_i+{\sum }_{i=1}^k{\beta }_{ii}{x}_i^2+\sum \hspace{0.17em}{\sum }_{i<j}^{\mathrm{\infty }}{\beta }_{ij}{x}_i{x}_j+\epsilon .$(2)

Mechanical testing of materials

The impact strength was determined according to the American Society for Testing and Materials (ASTM) D4812–99, with a specimen size of 63.50 × 12.70 × 3.2 mm (Annual Book of ASTM D 4812–99). The flexural strength was determined according to the American Society for Testing and Materials (ASTM) D790 with a specimen size of 125 × 12.70 × 3.2 mm (Annual Book of ASTM D 790–03). Experimental outcomes of the 27 experiments measured by impact testing and flexural testing are given in Table 4. The factor is written as an uncoded factor value.

Table 4. Mechanical strength of CMNFR.

Trial number	Uncoded value of factor			Impact Strength (J/mm^2)	Flexural Strength (MPa)
	A	B	C
1	1	1	2	2.6 × 10^−2	6.5 × 10^1
2	1	1	2	2.4 × 10^−2	3.4 × 10^1
3	1	1	2	2.3 × 10^−2	3.9 × 10^1
4	1	2	1	1.2 × 10^−2	6.7 × 10^1
5	1	2	1	1.2 × 10^−2	7.1 × 10^1
6	1	2	1	9.6 × 10^−3	7.3 × 10^1
7	1	3	3	1.4 × 10-^2	7.1 × 10^1
8	1	3	3	1.4 × 10^−2	7.5 × 10^1
9	1	3	3	1.4 × 10^−2	7.4 × 10^1
10	2	1	1	8.9 × 10^−3	5.7 × 10^1
11	2	1	1	1.1 × 10^−2	6.3 × 10^1
12	2	1	1	9.9 × 10^−3	5.3 × 10^1
13	2	2	3	1.1 × 10^−2	6.8 × 10^1
14	2	2	3	1.2 × 10^−2	6.8 × 10^1
15	2	2	3	1.3 × 10^−2	7.0 × 10^1
16	2	3	2	1.7 × 10-^2	9.1 × 10^1
17	2	3	2	1.9 × 10^−2	9.4 × 10^1
18	2	3	2	1.6 × 10^−2	9.1 × 10^1
19	3	1	3	1.3 × 10^−2	7.2 × 10^1
20	3	1	3	1.2 × 10^−2	7.3 × 10^1
21	3	1	3	1.4 × 10^−2	7.2 × 10^1
22	3	2	2	1.9 × 10^−2	7.2 × 10^1
23	3	2	2	1.8 × 10^−2	7.3 × 10^1
24	3	2	2	1.7 × 10^−2	7.0 × 10^1
25	3	3	1	5.8 × 10^−3	6.8 × 10^1
26	3	3	1	8.2 × 10^−3	6.4 × 10^1
27	3	3	1	5.6 × 10^−3	6.6 × 10^1

VARI method

In this study, the VARI manufacturing method was used because of the advantages of this method. Biocomposite lamination in the VARI process is shown in diagram Figure 3. The manufacturing process and the tools used can be seen in Figure 4. The VARI method is a composite manufacturing technique that utilizes vacuum pressure to flow the resin into a laminate and reduce the air content in the composite laminate kenaf, jute fiber, and the resin. The air content in the composite laminate will form voids in the laminate structure, and these voids will affect the mechanical properties of the composite material. Therefore, the use of the VARI method will produce a composite with a high ratio of matrix to reinforcement, consistent mechanical properties, few voids, and an equal matrix thickness. Figure 5 shows the specimen in the drying stage for 24 hours. The dried specimen is shown in Figure 6 and the specimen for mechanical testing is shown in Figure 7. The stages of this research are presented in Figure 8.

Figure 3. Vacuum infusion laminates steps.

Figure 4. VARI process.

Figure 5. Drying process.

Figure 6. Specimen release.

Figure 7. Specimen cutting results.

Figure 8. A schematic diagram of this research.

To make biocomposite reinforced with kenaf fiber and flax fiber using the VARI manufacturing method using the Taguchi method approach, this study includes the following steps:

1. Designing experimental designs using the Taguchi method

2. Soaking fiber in NaOH solution

3. Drying the fiber

4. Manufacturing natural fibers using the VARI method

5. Drying and hardening of composite molds

6. Performing the post-curing process.

Result and discussion

Manufacturing of composite materials with natural fiber reinforcement is carried out in several steps. The first step is to perform an SN ratio analysis, and this analysis produces the optimal condition setting value by adjusting the criteria for the most significant SN ratio in Table 5. The second step is multi-regression analysis to determine the relationship of each parameter setting, a variable, with the impact and flexural strength values. The third step is to test the optimal value by analyzing the maximum confidence level (less than 5%). The fourth step is the analysis of the effect of each factor on parameter settings. The fifth step is determining the optimal parameters for manufacturing composite materials with natural fiber reinforcement. The last step is a microstructural analysis of the fracture that occurs in the test specimen.

Table 5. ANOVA analysis of impact strength and flexural strength.

Control Factor	Level 1	Level 2	Level 3	Delta	Rank
	Impact strength
SN ratio for impact strength (dB)
(A)	−36.15	−37.96	−38.97	2.82	2
(B)	−36.75	−37.51	−38.82	2.07	3
(C)	−41.05	−34.24	−37.79	6.81	1
Means (μm)
(A)	0.016537	0.0131	0.01242	0.004117	2
(B)	0.015714	0.013729	0.012614	0.003099	3
(C)	0.009349	0.019749	0.012958	0.0104	1
	Flexural strength
SN ratio for flexural strength (dB)
(A)	35.52	37.05	36.90	1.52	2
(B)	34.91	36.91	37.65	2.74	1
(C)	36.14	36.26	37.06	0.92	3
Means (μm)
(A)	63.14	72.74	70.06	9.60	2
(B)	58.72	70.11	77.13	18.41	1
(C)	64.59	69.98	71.37	6.78	3

The SN ratio in the Taguchi method was used for ANOVA calculations. The ratio is used to analyze the scaling factor because there is a proportional variation between the mean and standard deviation of the material strength value.

SN ratio analysis

The experimental response in the form of composite material strength values obtained from experiments with different manufacturing parameters is shown in Table 4. High CMNFR composite material strength values were obtained from the optimal manufacturing composition. In this study, there are two conditions of optimal manufacturing parameters that produce maximum material strength which is simplified as follows: A₁B₁C₂ (impact strength) and A₂B₃C₃ (flexural strength), and the illustration of the SN ratio can be seen in Figure 9. The significance level of each parameter is obtained from the best results, and the optimal design is presented in bold in Table 5. Each optimal parameter setting tends to the maximum SN ratio. The optimal material strength value is proportional to the maximum SN ratio because the selected SN ratio criterion is larger is better. This indication shows that the parameter quality is optimal with maximum material strength (Bawono et al. 2019).

Figure 9. Plots of mean and SN ratios of composite material strength: (a) impact; (b) flexural.

Optimal composition selection based on Taguchi method

It has been known that the optimal manufacturing composition through SN ratio analysis to produce high material strength values are A₁B₁C₂ and A₂B₃C₃ (impact and flexural strength). In the analysis of the SN ratio on the impact strength data, it is known that the parameters that affect significantly NaOH concentration, post-curing temperature, and fiber type. The selection of a low level of NaOH concentration, a low post-curing temperature then the optimal impact strength of the composite material will be achieved, the same thing has been reported in previous studies by Lascano et al. (2019); Mahjoub et al. (2014). However, the increase in NaOH concentration and post-curing temperature can reduce the impact strength. The selection of the type of kenaf fiber has a positive impact on the maximum impact strength, as shown in Table 5; the fiber type has a higher level of influence. This indicates that when targeting the maximum impact strength, it is necessary to pay attention to the setting of these parameters (Asumani and Paskaramoorthy 2021; Jaafar, Rizal, and Zainol 2018; Ramesh, Durga Prasad, and Narayana 2018). However, for flexural strength, the selection of a level at a moderate NaOH concentration, high post-curing temperature, and supported the choice of the type of fiber (jute-kenaf fiber) results in maximum flexural strength, as shown in Figure 9. After obtaining the optimal factors and their levels, the mean value and signal-to-noise ratio predictions for the optimal conditions are calculated. This calculation is intended to predict the mean value and signal-to-noise ratio. Prediction of the optimal material strength (Strength_pred) of the impact strength can be expressed as follows:

(3) $Strengt{h}_{pred}=T_{\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\mathrm{\_}exp}+\left(\overline{A_1}-T_{\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\mathrm{\_}exp}\right)+\left(\overline{B_1}-T_{\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\mathrm{\_}exp}\right)+\left(\overline{C_2}-T_{\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\mathrm{\_}exp}\right)$(3)

Where $T_{\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\mathrm{\_}exp}$ = 0,014019; $\overline{A_1}$ = 0.016537; $\overline{B_1}$ = 0.015714; $\overline{C_2}$ = 0.019749. Hence, the predicted value of Strength_pred is 0.024J/mm². As the prediction of the optimal manufacturing material strength of the flexural strength is $T_{\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\mathrm{\_}exp}$ = 68.65; $\overline{A_2}$ = 72.74; $\overline{B_3}$ = 77.13; $\overline{C_3}$ = 71.37. Hence, the predicted value of Strength_pred is 83.9MPa.

Confidence Interval (CI) is used to determine the mean accuracy of a sample. The study aims to estimate the true value of measurement by making a series of measurements on a sample and calculating the average of those measurements. Confidence Interval (CI) is considered to be able to predict the optimal value, it can be calculated as follows (Bawono et al. 2019):

(4) $CI=\sqrt{F_{\alpha ,dof}{V}_{error}\times \left(\frac{1}{n_{eff}}\right)},$(4)

(5) $n_{eff}=\frac{\mathrm{N}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}{1+\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{d}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{s}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{e}}.$(5)

Table 6 presents the results of calculating confidence intervals using the Minitab 19 software. The confidence intervals of the material strength Strength_pred for the impact strength were calculated to be CI_Strength=± 0.014 and CI_Strength=± 68.65 for the flexural strength. Table 7 presents the validation results from the experiment according to the optimal combination of parameters. The confidence intervals for impact strength and flexural strength were calculated to be 0.014 and 68.65. The results of the response validation test give a 95% CI value.

Table 6. CI calculated confidence interval.

Response	N	95% CI for μ
Impact strength	27	(0.012; 0.016)
Flexural strength	27	(63.55; 73.75)

Table 7. Comparisons of results of the experimental and Taguchi predicted value.

Response	Experimental result	Calculated value	Confidence interval (CI)	Difference Strengthexp-Strengthcal	Optimization
Impact strength	Strengthexp = 0.026	Strengthcal = 0.024	CIStrength = 0.014	0.002	0.002 < 0.014 Successful
Flexural strength	Strengthexp = 90.6	Strengthcal = 83.9	CIStrength = 68.65	6.7	6.7 < 68.65 Successful

Analysis of variance in the RSM

ANOVA was used to evaluate the regression model significance of the parameters (NaOH concentration, post-curing temperature, fiber type) and individual model coefficients. Table 8 presents the ANOVA analysis of the experimental results for impact and flexural strength. The experimental design was evaluated at a 95% confidence level. The value of the contribution is considered in identifying the level of significance of the variable. The results of the analysis show that the most influential parameter influencing the impact strength value is the concentration of NaOH (A) 11.54%; post-curing temperature (B) 6.54%; and fiber type (C) 8.86%. Thus, the concentration of NaOH has a higher contribution to the process of making CMNFR. Likewise, the most significant parameter affecting the flexural strength value is A (4.99%); B (35.37%); C (4.79%). Table 7 presents the validation results from the experiment according to the optimal combination of parameters. The confidence intervals for impact strength and flexural strength were calculated to be 0.014 and 68.65. The results of the response validation test give a 95% CI value.

Table 8. ANOVA analysis of impact strength and flexural strength.

Source	DF	Seq. SS	Contribution	Adj. SS	Adj. MS	F-Value	P-Value
ANOVA for impact strength
Model	8	6.34 × 10^−4	95.94%	6.34 × 10^−4	7.90 × 10^−5	53.22	0
Linear	3	1.78 × 10^−4	26.94%	1.50 × 10^−4	5.00 × 10^−5	33.58	0
A	1	7.60 × 10−5	11.54%	7.60 × 10−5	7.60 × 10−5	51.20	0
B	1	4.30 × 10−5	6.54%	2.20 × 10−5	2.20 × 10−5	14.70	1 × 10−3
C	1	5.90 × 10−5	8.86%	3.40 × 10−5	3.40 × 10−5	22.62	0
Square	3	4.56 × 10^−4	68.94%	3.44 × 10^−4	1.15 × 10^−4	76.94	0
A*A	1	1.10 × 10^−5	1.72%	1.10 × 10^−5	1.10 × 10^−5	7.65	1.30 × 10^−2
B*B	1	1 × 10^−6	0.17%	1 × 10^−6	1.00 × 10^−6	0.55	4.70 × 10^−1
C*C	1	4.43 × 10^−4	67.04%	3.22 × 10^−4	3.22 × 10^−4	216.42	0
2-Way Interaction	2	0	0.07%	0	0	0,15	8.62 × 10^−1
A*B	1	0	0.07%	0	0	0,21	6,55 × 10−1
A*C	1	0	0.00%	0	0	0	9,72 × 10^−1
Error	18	2.70 × 10^−5	4.06%	2.7 × 10^−5	1 × 10^−6
Total	26	6.61 × 10^−4	100%
ANOVA for flexural strength
Model	8	3.67 × 10^3	85.16%	3.67 × 10^3	4.59 × 10^2	12.91	0
Linear	3	1.95 × 10^3	45.16%	2.25 × 10^3	7.52 × 10^2	21.14	0
A	1	2.15 × 102	4.99%	2.15 × 102	2.15 × 102	6.06	2.40 × 10−2
B	1	1.53 × 103	35.37%	1.90 × 103	1.90 × 103	53.38	0
C	1	2.07 × 10^2	4.79%	3.80 × 10^−1	3.80 × 10^−1	0.01	9.19 × 10^−1
Square	3	2.79 × 10^2	6.47%	4.34 × 10^2	1.45 × 10^2	4.07	2.30 × 10^−2
A*A	1	2.26 × 10^2	5.25%	2.26 × 10^2	2.26 × 10^2	6.37	2.10 × 10^−2
B*B	1	2.86 × 10^1	0.66%	4.41 × 10^1	4.41 × 10^1	1.24	2.80 × 10^−1
C*C	1	2.40 × 10^1	0.56%	1.41 × 10^2	1.41 × 10^2	3.96	6.20 × 10^−2
2-Way Interaction	2	1.45 × 10^3	33.53%	1.45 × 10^3	7.23 × 10^2	20.34	0
A*B	1	9.38 × 102	21.74%	2.32 × 102	2.32 × 102	6.54	2 × 10−2
A*C	1	5.08 × 10^2	11.79%	5.08 × 10^2	5.08 × 10^2	14.30	1 × 10^−3
Error	18	6.40 × 10^2	14.84%	6.40 × 10^2	3.56 × 10^1
Total	26	4.31 × 10^3	100%

RSM-based modeling for impact and flexural strength

The level of influence can be determined from the contribution factor (% contribution) to the mathematical model of the maximum value of material strength. A larger contribution value indicates a greater influence of the factor. The second-order regression model can be seen in equations (4) and (5). This regression model is a function of three manufacturing parameter variables (A, B, and C).

(6) $Strengt{h}_{(impact)}=-0.00386-0.00805\mathrm{A}-0.00378\mathrm{B}+0.03583\mathrm{C}+0.001378\mathrm{A}\ast \mathrm{A}+0.000425\mathrm{B}\ast \mathrm{B}-0.008465\mathrm{C}\ast \mathrm{C}+0.000261\mathrm{A}\ast \mathrm{B}-0.000021\mathrm{A}\ast \mathrm{C},$(6)

(7) $Strengt{h}_{(flexural)}=18.2\hspace{0.17em}+21.1\mathrm{A}+16.4\mathrm{B}+0.9\mathrm{C}-6.14\mathrm{A}\ast \mathrm{A}+3.13\mathrm{B}\ast \mathrm{B}-5.59\mathrm{C}\ast \mathrm{C}-7.19\mathrm{A}\ast \mathrm{B}+10.63\mathrm{A}.$(7)

This research gets a response strength of the material in both tests, which gave an R² of 95.94% (impact) and 85.16% (flexural). This shows that the R² value is close to 100%, and therefore, the two regression models formed above can be used to predict the material strength of certain design parameters. After forming the second-order regression model and the ANOVA generated from the response data, the predicted optimum parameter values can be derived using the surface plot curve and the desirability function. The effect of interaction variables on the strength of the material is illustrated using a three-dimensional (3D) plot that fits the second-order model (Equations 4(4) $CI=\sqrt{F_{\alpha ,dof}{V}_{error}\times \left(\frac{1}{n_{eff}}\right)},$(4) and 5(5) $n_{eff}=\frac{\mathrm{N}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}{1+\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{d}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{s}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{e}}.$(5) ). The results of observations from Table 8 show that the factors that influence the value of material strength (impact) are factors A, B, C, and flexural strength are factors A and B. The interaction factor A*B (impact and flexural strength) is considered to be the most influencing parameter material strength value. The P-value of a factor less than 0.05 indicates that the factor is deemed to have a significant effect. The analysis can be illustrated using a 3D plot, as shown in Figure 10.

Figure 10. 3D RSM plot of the strength of natural fiber composite materials: (a) NaOH vs. post-curing temperature for impact strength; (b) NaOH vs. post-curing temperature for flexural strength.

Optimization using desirability function analysis

In this study, the desirability function of the selected material strength is “bigger is better” because the optimum material strength can be achieved within the optimal composite manufacturing composition parameters. The desirability function scale ranges from 0 to 1 (Bawono et al. 2019). The “bigger is better” desirability function can be shown in Figure 11.

Figure 11. Plots of response optimization (D= composite desirability; d= individual desirability; High = highest value parameter; Cur= optimal current value of control parameter; Low = lowest value parameter, y= response parameter, A= NaOH concentration; B= post-curing temperature; C= fiber type).

The results of the desirability function analysis show that the optimal impact strength prediction is 0.0241 J/mm², this is obtained at factors A, B, and C, and each at levels 1, 1, and 2. At the same time, the optimal flexural strength is 94.865 MPa, obtained at factors A, B, and C and levels 1, 3, and 1. In addition, the desired values for the material strength (impact and flexural) are 0.93036 and 1.0000, respectively. This response is considered perfect for the maximum value target because it is close to 1.0 and shows with red line in Figure 11. The use of the Taguchi-RSM method in this study has been proven to be accurate and effective in determining the optimum composition in the CMNFR manufacturing experiment. Similar methods have been applied in previous studies (Anggoro et al. 2019; Anggoro et al. 2021; Anggoro et al. 2021; Bawono et al. 2019).

Observations on the plot of response optimization shown in Figure 11 show that maximum impact strength can be achieved by setting the parameters of NaOH concentration and post-curing temperature at low levels. On the other hand, increasing the level of NaOH concentration and post-curing temperature will decrease the maximum impact strength (Asumani and Paskaramoorthy 2021; Jaafar, Rizal, and Zainol 2018; Ramesh, Durga Prasad, and Narayana 2018). Meanwhile, to achieve maximum flexural strength, plots of response optimization show the post-curing temperature at a higher level. These different parameter settings are adjusted to the mechanical properties to be achieved.

Microstructural analysis

Microstructure observations were carried out to determine the micro-condition of the composite structure. These observations usually involve the presence of contaminants and voids. Microstructure observations were carried out on each specimen. It was found that there were significant differences in microstructure in the specimens with a strength value of 0.026 J/mm² and 0.006 J/mm² (impact test). Likewise, the flexural test found significant differences in the specimens with values of 94.2 MPa and 34.5 MPa. Based on these observations, the analysis was carried out through micro-photographs as shown in Figure 12. The results of the micro-photo analysis on the impact test specimen had contaminants and voids in the specimen with a strength value of 0.006 J/mm².

Figure 12. Micro photos of composite specimens; (a) impact test specimen, (b) flexural test specimen.

On the other hand, the impact test specimen with a strength value of 0.026 J/mm² does not have voids and contaminants. Likewise, the flexural test specimen experienced the same phenomenon in the specimen. Thus, the presence of contaminants and voids has a negative impact on the mechanical properties. This is following previous studies (Gholampour and Ozbakkaloglu 2020; Senthilkumar et al. 2018; Van de Werken et al. 2019; Yu et al. 2019), which stated that contaminants and voids could affect the mechanical properties of the specimen. More contaminants and voids will degrade the mechanical properties of the material. The functions of kenaf and jute fiber will be mixed in a resin matrix. Strength will increase, but toughness will decrease.

The results of the research analysis it is necessary to be careful about air leaks that may occur and the cleanliness of the composite molding tool so that voids and contaminants can be reduced or eliminated.

Conclusion

This study uses Taguchi and RSM methods to optimize CMNFR manufacturing parameters. The results of this study can be summarized as follows:

1. The optimum material strength based on the Taguchi method approach shows the optimum impact strength value of 0.025 J/mm² and the optimum flexural strength value of 83.948 MPa. The optimal combination is in position A₁B₁C₂ (impact strength); A₂B₃C₃ (flexural strength).

2. Based on the RSM and the composite desirability method, the optimal manufacturing parameters of the VARI method, namely, a NaOH concentration of 4%, the post-curing temperature of 80°C, and fiber type of kenaf with an average impact strength of 0.0241 J/mm², can be achieved at a desirability (dF) of 0.93036. Likewise, for flexural strength using 4% NaOH concentration parameters, a post-curing temperature of 120°C and jute fiber type with an average flexural strength of 94.8650 MPa can be achieved at a desirability (dF) of 1.0000. Thus, the main factors affecting impact strength and flexural strength, respectively, contributed 93% and 100%.

3. The results of the microstructure analysis found that the presence of voids and contaminants in the specimens had a negative impact on the mechanical properties of the composites, as evidenced by specimens containing contaminants and voids having low mechanical properties. The contaminants and voids will degrade the mechanical properties of the material. The functions of kenaf and jute fiber will be mixed together in a resin matrix. Flexural strength will increase, but toughness impact will decrease.

Future research will focus more on the application of use of this material and the process of designing and manufacturing automotive accessory components such as mirrors, spoilers, and others.

Highlights

• The use of composite materials with natural fiber reinforcement (CMNFR) has experienced rapid development in the automotive industry

• This study uses the vacuum-assisted resin infusion (VARI) process to fabricate composites using woven kenaf and jute fiber

• Two optimization technique methods are used to obtain the optimal composite material composition

Nomenclature

CMNFR	=	Composite materials with natural fiber reinforcement
RSM	=	Response surface methodology
KF	=	Kenaf fiber
JF	=	Jute fiber
NaOH	=	Natrium Hydroxide

Author contribution statement

PW. Anggoro: Conceived and designed the experiments, analyzed and interpreted the data, conceptualized and wrote the paper.

B Bawono : Performed the experiments formal analysis and sourced the corresponding materials.

Haniel: Conceived and designed the experiments and wrote the paper

Acknowledgements

This paper was supported by PUTP ATMI Surakarta Polytechnic and the laboratory for basic machine phenomena at the University of Muhammadiyah Yogyakarta, which we really appreciate.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is also part of research funded by the Directorate General of Higher Education, Research, and Technology in the Funding Program for Research and Community Service for Fiscal Year 2022, grant Number: 1989.4/LL5-INT/PG.02.00/2022,165/LPPM-Pen/Eks