Green Composites in Aviation: Optimizing Natural Fiber and Polymer Selection for Sustainable Aircraft Cabin Materials

Abstract

The increasing demands on global resources due to technological development driven by consumer expectations and demands have resulted in significant problems with ecological sustainability and material availability. The creation of biocomposites has resulted in notable advancements in the green industry within the materials science area this century, owing to concerns regarding sustainability and the environment. Globally, there is a surge in the creation of highly efficient materials derived from natural resources. In aviation applications, plant fiber-supported polymer composite materials are becoming increasingly popular. Aerospace materials are typically used in aircraft construction as structural materials to support loads throughout different flight phases. There are many diverse mechanical qualities of natural fibers; therefore, selecting one for the interior parts of an aircraft cabin based only on its attributes leads to a multiple-attribute decision-support issue. In this paper, the effective natural fiber and polymer choice for use as reinforcing materials in composite materials is represented as the composite materials’ improvement to aircraft cabin luggage for aerospace implementations. This study can guide material designers in investigating different hybrid materials with the most effective natural fiber and polymer obtained by hierarchical strategy by elucidating the effective material choice to meet the criteria determined for the aircraft cabin luggage. For this purpose, the definitive rankings of the twelve polymers and sixteen natural fibers in terms of performance score were assessed using a hierarchical strategy methodology.

1. Introduction

Composites with biofiber have seen incredible metamorphosis in the past few decades. With the thorough investigation, development, and subsequent application of novel compositions and techniques, these materials have grown increasingly sufficient [1]. Bio-composites gained considerable importance due to the petroleum crisis and are now widely used as engineering materials with a variety of characteristics [2]. But like other materials, they are always under pressure from the worldwide market to compete, which means that ongoing research is required. Dealing with plant fiber-supported polymer composite materials presents a significant challenge owing to their wide-ranging qualities and characteristics [3,4,5]. Many factors, such as the kind of fiber, the processing techniques, the location of the plant fibers’ source, and any fiber alteration, affect a biocomposite’s characteristics. Therefore, as new procedures and goods are developed, the growing utilization of natural resources becomes significant for these nations.

Potency implementations of fiber-supported polymer composites are given in

Table 1. Potency implementations of fiber-supported polymer composites.

Samples	Implementation
Storage silos, biogas containers, fuel containers, post boxes, etc.	Storage devices
Snowboards, frames, bicycles, balls, tennis rackets	Leisure and sports goods
Laptop cases, mobile cases	Electronics appliances
Carpets, mats, sacking, Hessians, bags, ropes, pipes, covers, units, baths, showers, helmets, paperweights, suitcases, lampshades, partitions, food trays, profiles of doorframes, interior paneling, door panels, fencing elements, chairs, tables	Utility and household products
Panels for false and partition ceilings, door and window frames, floors, walls, partition boards, roof tiles, bridges, railings, transportable buildings that are resilient to natural disasters	Construction and building sector
Architectural moldings, interior paneling, boats, railway and automobile coach interiors, spare wheel pans, spare tire covers, parcel shelves, decking, trunk liners, pallets, car doors, dashboards, headliners, seat backs, door panels	Aviation, transportation, and automobile sector

[6].

For aeronautical applications, it is important to choose materials with maximum durability but less weight. Any aerospace vehicle’s design must prioritize weight reduction because it has a direct impact on fuel economy and cost. Research has demonstrated that a 1 kg weight reduction in a Boeing 747 aircraft—which is commonly utilized for freight conveyance—reduces carbon emissions by 940 g and aeronautic energy source consumption by over 300 g [7]. This is why the need for developed composites that allow for important aircraft weightage degradation without sacrificing structural integrity is currently rising in the aerospace sector. Features of an aircraft structure are given in

Table 2. Features of an aircraft structure.

Applicability	Requirement	Effect
Overall programs for aerospace	Less weightage	Use of low-density materials Stiffened structures or thin-walled box Semi-monocoque construction composites High weight strain and weight/stiffness
Every space program	Elevated dependability	Certification: evidence of design Ensure accurate data for tight quality control Extensive testing
Vehicles for passengers	Safety of passengers	Comprehensive testing: reliability Using materials that are fire-retardant
Reusable spacecraft aircraft	Durability: corrosion and fatigue vacuum radiation thermal degradation	High-integrity thin materials Thorough testing in the necessary setting Damage and safe-life, life extension issues Issues with damage, safe life, and life extension There is no fatigue limit for al (alloys) Thorough fatigue testing and analysis
Reusable spaceship aircraft	Performance in aerodynamics	Machinability: N/C Milling and Molding Intricately curved shapes Dynamics Extremely intricate loading Deformed shape (aeroelasticity) Control surfaces and flexible, thin, wings
Every aerospace initiative	Multiple functions or roles	Application: composites with useful characteristics Effective design
Airplanes, primarily fighters but some passenger	Fly-by-wire system	EMI protection Prolonged usage of devices and computers Elevator servo Arrangement–control relationships
Particular use in military aerospace	Stealth	Stealth coating Aircraft shape and specific surface
Aircraft	Weather-related operations	Erosion resistance, lightning protection

[8]. It must be acknowledged that materials with a unique property set are required to satisfy the requirements listed in Table 2.

Nowadays, polymer matrix composites make up a sizable portion of aircraft materials. Because of their renewability, lower cost, and high ratio of strength-to-weight, natural fibers have become an effective replacement for artificial fibers in the manufacturing of composite materials like carbon fibers and E-glass. However, because there are so many diverse kinds of plant fibers that can be hybridized with diverse matrix materials, choosing the best natural fiber for an aircraft implementation can be hard. The reinforced polymer material with fiber has drawn numerous considerations in aviation implementations because natural fiber has benefits over artificial fibers, like being relatively light, inexpensive, less likely to damage operational tools, having high notional mechanical features like flexural and tensile strengths, developing the superficies finish of a composite’s molded areas, abundant in sustainable sources, flexible throughout treatment, biodegradable, and posing few health risks. The addition of strong and lightweight plant fiber to polymers (thermoplastic and thermoset) can result in plant fiber polymer composite materials with maximum stiffness and strength [9]. Nonetheless, there are several issues with natural fibers, and they lack some important qualities. Some plants’ structure allows the fiber to absorb moisture from its environment, which results in fragile bonds between the polymer and fiber. Moreover, couplings between natural fibers and the matrix and polymers are considered challenging due to their dissimilar chemical structures. These are the reasons behind the inefficient strength sent through the window between the composite materials. Plant fiber changes through particular processes are therefore unquestionably required. Utilizing functional groups as reagents, which can respond to fiber structures and alter their structure, is typically at the core of these alterations. Therefore, fiber changes result in a decrease in the natural fibers’ ability to absorb moisture, which greatly improves the fiber’s incompatibility with the polymer material [10].

Frequently used polymeric matrices in the aerospace industry are given in

Table 3. Frequently used polymeric matrices in the aerospace industry.

Thermosets				Thermo-Plastics
Creates Cross-Linked Networks During Heating-Curing Polymerization				No Alteration in Composition
Polyimides	Polyester	Phenolics	Epoxies	PPS, PEEK
Brittle Complicated to handle 300 °C high-temperature application	Recommended for general use at room temperature Simple to employ Low cost	Difficult to obtain composites of high quality Reduced viscosity High-temperature consumption Simple to operate Less expensive	Comparatively expensive Moderately high temperature Most often used (80% of all composites)	Process is challenging since a high temperature of 400–300 °C is needed High resilience to damage
	High shrinkage (about 7.5 percent)	Volatiles released while curing More shrinkage	No volatiles are released when curing Less shrinkage
	Low Temperature Brittle Broad spectrum of properties, albeit less so than epoxies Natural stability in the face of oxidation Strong resilience to chemicals	More brittle than epoxy Good resistance to fire and naming Natural stability in the face of Oxidations	May be polymerized in a number of ways, yielding a wide range of structures, morphologies, and characteristics
	Challenging to prepare	Less stable storage and challenging preparation	Sufficient storage stability for preparing	Endless existence in storage but challenging to prepare
	Less moisture-sensitive than epoxy	Absorbs moisture, but molasses has no discernible impact on its operational range	Long-term ultraviolet degradation. Complete wetness (5–6%), which causes temperature pastries to expand and degrade	Absence of moisture absorption

[8,11,12,13,14,15,16,17,18].

An appropriate candidate material choice for a given application has grown in importance in advanced material research in order to achieve both eco-friendly design and consumer satisfaction. The process of multicriteria decision making is commonly employed to determine which materials are best suited for a certain application [11,12]. AHP was the most often utilized multiple attribute decision support mechanism in a variety of material selection scenarios, along with TOPSIS, WPIM, and AHP, according to Mansor et al. [13]. Thomas invented it in the 1970s [14]. Chang et al. used sensitivity analysis to assess their choice. Sensitivity analysis uses several criterion weights to validate the AHP results [15]. AHP is typically used to determine which alternative is best suited for a given project or application. Establishing objectives is the primary stage in the AHP strategy, followed by choosing criteria and options. The weighting of the attribute and the alternatives’ respective notional significance determine the ultimate ranking of the options. By combining AHP and TOPSIS, Kumar et al. focused on a calculational framework for choosing the ideal aspirant cloud service [16]. The material selection of optimal matrix for aluminum-integrated metal matrix composite materials via AHP was examined by Babu et al. Their attention was on aluminum-integrated metal matrix composite materials [17]. In order to overcome the specification problem of weighting components and gathering various design criteria into a single composite, Sun suggested an improved multi-criteria decision analysis technique [18]. An index was proposed by Sun and Gollnick to assess a technique’s effectiveness in the method selection process [19]. After performing a thorough analysis of MCDM approaches, Ogrodnik came to the conclusion that AHP is one of the most broadly utilized methodologies available today [20]. The novel AHP-sourced method for choosing construction materials according to performance was presented by Lee et al., and it is used in a concrete formwork system case study [21]. Using AHP, Dinh et al. ranked a list of 18 sustainability attributes that applies to the choice of maintainable materials in Vietnam according to their level of significance for the nation’s building industry [22]. Stainless steel and aluminum-integrated panels were the two materials that Ruslan et al. determined to be the most sustainable for usage as façade materials using value-based analysis and AHP [23]. AHP was used by Mayhoub et al. to create a new evaluation framework for choosing sustainable façade materials that are dependent on four green construction rating mechanisms [24]. For post-tension bridges, a related approach was demonstrated through Renkas-Janowska et al., who chose the optimal high-efficiency concrete using FAHP and Fuzzy-TOPSIS [25]. Singh and colleagues combined TOPSIS and FAHP while choosing composite materials for use in structural components [26]. AHP and gray-correlation TOPSIS were integrated by Tian et al. to choose eco-friendly décor materials [27]. Akadiri et al. used the analytic hierarchy process (fuzzy extended) to select maintainable construction materials [28]. A framework for decision making utilizing FAHP was presented by Figueiredo et al. to help with the selection of building materials [29]. In the automobile business, TOPSIS has been used to choose the most advantageous strategic fuel cell technology [30]. In order to choose the finest roofing materials for the UK property market, Rahman et al. used TOPSIS [31]. Lee used ANP to assess the competing types for airport development [32]. Materials were chosen by Liu et al. using the VIKOR method [33]. VIKOR was used by Khodabakhshi et al. to pick the best materials [34]. Using a hybrid MCRAT, LOPCOW, MEREC, and PSI modeling approach, Ulutas et al. determined the best performing bio-fiber for popular construction isolation materials. They then selected the insulation materials using the PSI-CRITIC-based CoCoSo technique [35,36]. A novel hybrid MCDM model was presented by Aksakal [37] for the evaluation of insulation materials in a healthier environment. Balo was assessed for utilizing an AHP in the production of ecological insulation materials [38]. Using AHP, Dweiri et al. suggested a decision-making modeling method to find the automobile industry’s most efficient supplier [39]. For Greek road transportation, the optimum alternative fuel was chosen using AHP by Tsita et al., taking into account both the economic and policy considerations [40]. In order to select the most suitable ceramic waste to substitute ordinary concrete in terms of compressive strength and environmental effects, some research has combined AHP and TOPSIS by Rashid et al. [41]. During the selection process, they took the natural fiber’s mechanical and physical qualities into account. AHP was applied in addition to material selection to determine which of the creative planning notions for the polymer composite materials intended for lever applications of car brakes [42]. The fourteen plant fibers and seven attributes were considered. For the vehicle spall liner, they selected kenaf as a feasible plant fiber to hybridize with Kevlar. AHP was utilized to choose the natural fiber for the dashboard used in automobiles by Hani et al. [43]. Dalalah et al. selected the natural fiber for the car fracture liner using AHP [44]. Balo et al. analyzed energy-effective natural fibers for green building external walls [45,46,47,48]. There are a number of characteristics to consider when selecting component materials for a combined material, but deciding which to select in order to obtain the greatest result out of the available options can be challenging [49,50]. Multiple-attribute decision-support strategies can be used to select the factors of combined materials since they provide decision-makers with a reasonable suggestion from a restricted number of options, too. Many multiple-attribute decision-making models evaluate the performance of options and provide the best possible resolution among a variety of options. Amarnath et al. investigated the best solution for flax components in composite materials using the TOPSIS methodology [51]. For the nutrient pack, AHP modeling was used to select the best fiber among nine plant fibers. The authors also ran a sensitivity test on the modeling and found that the AHP model’s system of priorities was stable, according to Salwa et al. [52]. Rocchi et al. identified economic and environmental criteria groups to evaluate the ecological and financial suitability of sustainable isolation [53]. Kumar et al. suggested an optimization schema for comparing the features of different insulation materials, with a focus on lifecycle cost, comfort, operational energy, embodied energy, and carbon [54]. In Lithuania, Ruzgys et al. investigated modernized building planning resolutions. Utilizing the connected TODIM-SWARA method, the researchers ranked six external wall isolation options for construction modernization (mineral wool and fiber cement panels; polystyrene foam; and thin plaster). A ventilated mechanism with fibro-cement panels and mineral wool insulating material with 0.13 m thickness was discovered to be the most effective option for residence modernization [55]. Bringezu and Sameer demonstrated the assessment of material resources and the importance of indicators, such as performance indicators, criteria, parameters, and inputs, in material choice to deal with maintainability [56].

In this research, an overview is first given of the general attributes of the reinforcing fibers utilized in biocomposites, including their source, kind, structure, content, and mechanical properties. Then, the analysis was made by following the steps below:

-

To conduct an in-depth investigation of the mechanical properties of twelve polymers and sixteen natural fibers in terms of tensile strength, Young’s modulus, density, and elongation at a break from the existing literature.

-

To examine and gather the chemical (micro-fibrillar angle, lignin, hemicellulose, cellulose, and moisture content) and physical (width of lumen, fiber length, thickness of a single cell wall, and fiber diameter) characteristics of sixteen natural fibers.

-

To determine the influence of data variation on the obtained mechanical properties on the performance score of each polymer.

-

To determine the influence of data variation on the obtained mechanical, physical, and chemical properties on the performance score of each natural fiber.

-

To assign weights to the criteria using the hierarchical strategy methodology to indicate the relative importance of the criteria.

-

To assess the performance scores of all the variants of the twelve polymers and sixteen natural fibers.

The main objective of this research is to apply multi-criteria decision making for the selection of suitable materials for biofiber and polymer-based composites that can be designed for use in aircraft cabins and to present the most suitable materials to those interested in a hierarchical ranking.

2. Materials and Methods

The methodology of hierarchical strategy is a methodical approach to material selection. The hierarchical approach, as previously noted, can be utilized to identify appropriate natural fiber and polymer materials for aviation cabin luggage covers. Rather than dictating the “right decision”, people deal with complex decisions; the hierarchical approach technique assists them in making one. Developed by Saaty (1980), it is based on human psychology and mathematics and has undergone substantial research and development since then. The hierarchical strategy approach is a way for the decision-maker to organize complex problems into a hierarchy, or a series of integrated levels, and is useful for prioritizing alternatives when various factors need to be taken into account. The objective, the criteria, and the options are typically the first three tiers of the hierarchy. The objective of the material selection problem is to choose the best material overall. Physical, chemical, mechanical, and other criteria are all possible. This approach serves to streamline and expedite the natural process of decision making and offers a framework for making wise choices in challenging circumstances (like material selection, for example).

The hierarchical strategy technique utilized in this study is credited to Saaty and is commonly referred to as the Saaty method.

The approach finds applications across all hierarchical levels. Each component of the quantitative features was given a value based on its importance using Saaty’s technique. These assessments were synthesized to determine the component with the highest priority. To find a solution to the choice dilemma, the decision-maker concentrates on them. More experts are involved in the decision-making process. The degree of soundness of the objective and criteria is shown by the weights assigned to them by the assessors, who are experts in their field. Any expert who is familiar with his subordinates (e.g., job expertise, work experiences, and results) can be verified by the responsible supervisor. Expert soundness can be expressed as a weight vector:

$$v^{experts}=\left(v_1,v_2,\dots ,v_r\right)$$

(1)

where v₁ is the first expert’s weight; v_r is the third expert’s weight.

$${\sum }_{j=1}^r{v}_j=1$$

(2)

Saaty’s approach was selected to determine the weights of the criteria. This approach considered the varying preferences among the criteria and determined a broad point scale for assessment (Formula (3)). As a result, even minute variations in the criteria’s preferences can be found and taken into consideration when determining the weights:

$$\left(s_{ij}\right)=\left\{\begin{array}{c}1-i and j are equivalent;\\ 3-i is mildly preferred to j;\\ 5-i is strongy preferred to j;\\ 7-i is very strongly preferred to j;\\ 9-i is absolutely preferred to j.\end{array}\right.$$

(3)

The values 2, 4, 6, and 8 are meant to assess transitional phases. Every pair of criteria, i and j, was compared using this procedure. The following guidelines were followed when writing their evaluation in accordance with Saaty’s matrix [57,58]:

$$s=\left(\begin{array}{ccc}1& s_{12} \cdots & s_{1k}\\ ⋮& \ddots \ddots & ⋮\\ {\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$s_{ik}$}\right.}& {\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$s_{2k} $}\right.}\cdots & 1\end{array}\right)$$

(4)

The normalized geometric average of the lines in the Saaty matrix was used to calculate the weights in this five-step approach [59,60,61]. In order to determine if the ith criterion is preferred over the jth criterion, Saaty’s matrix must first be filled in such a way that the diagonal values equal one (s_ij = 1). Next, the proper value of Saaty’s point scale (Formula (3)) must be chosen. Inverse values must be expressed if the jth criterion is chosen above the ith criterion:

$$s_{ji}={\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$s_{ij}$}\right.}$$

(5)

For every i, the value was calculated.

$$s_i={{\displaystyle \prod }}_{j=1}^k{s}_{ij}$$

(6)

For every i, the value was calculated.

$$R_i=\sqrt[k]{s_i}$$

(7)

where R is geometric average; k is total number of criteria.

In the next step, the value was calculated.

$${{\displaystyle \sum }}_{i=1}^k{R}_i$$

(8)

The criteria weights were determined during the last step according to the following formula:

$$v_i={\displaystyle \frac{R_i}{{{\displaystyle \sum }}_{i=1}^k{R}_i}}$$

(9)

Weights of criteria were determined by multiplying all elements in each row and determining the nth root of this product, where n is the number of elements. Then, the resulting geometric averages of each row was standardized (dividing the geometric averages of each row by the sum of all geometric averages) [61,62,63,64].

This procedure gives estimate weights of each criterion, which can be written in the form of a weight vector:

$$v=v_1, v_2, \dots , v_k$$

(10)

Saaty’s method can be used not only to determine preferences between criteria but also between variants, using the hierarchical strategy methodology [65,66,67].

3. Results and Discussion

3.1. Data

This section provides information about the data used as part of the methodology. The world’s largest producers of natural fibers for commerce are given in

Table 4. The world’s largest producers of natural fibers for commerce.

Fibers	Global Production (×103 t)	Region	Reference
Rice	16,000,000	China, India, Indonesia, Malaysia, Bangladesh	[68,69]
Corn	122,080	USA, China, Brazil, Argentina, India	[70]
Cotton	21,400,000	United States and Asia	[71]
Ramie	10,000	India, China, Brazil, Philippines	[72,73,74,75,76,77,78,79,80,81,82]
Kenaf	97,000	India, Bangladesh, United States	[72,73,74,75,76,77,78,79,82]
Bamboo	3000	India, China, Indonesia, Malaysia, Philippines	[72,73,74,75,76,77,78,79]
Oil palm	4000	Malaysia, Indonesia	[72,73,76,83,84]
Flax	83,000	Canada, France, Belgium	[72,73,74,75,76,77,78,79,80]
Abaca	7000	Philippines, Ecuador, Costa Rica	[72,73,74,75,76,77,85]
Banana	1920	Latin America, the Caribbean, Africa, Asia	[86]
Jute	230,000	India, China, Bangladesh	[72,73,74,75,76,77,78,79,87]
Pineapple	7400	Philippines, Thailand, Indonesia	[72,73,77,78,79,81]
Sisal	37,800	Tanzania, Brazil, Kenya	[72,73,74,75,76,77,78,79,81,87,88,89,90]
Coir	10,000	India, Sri Lanka, Philippines, Malaysia	[72,73,74,75,76,77,78,79,91,92,93,94,95,96]
Coconut	7700	Indonesia, Philippines, India, Sri Lanka	[95]
Sugar can bagasse	7,500,000	India, Brazil, China	[72,73,74,75,76,77,78,79]

. The chemical features, mechanical features, and physical features of natural fibers are presented in

Table 5. The chemical features of natural fibers.

Fiber Code	Fibers	Micro-Fibrillar Angle [°]	Lignin (wt%)	Hemicellulose (wt%)	Cellulose (wt%)	Moisture Content (%)	Reference
NF 1	Rice		20	19–28	35–45	7.9	[72,73,74,75,78,79,85,96,97]
NF 2	Corn		7.4	46	41.7	8.5	[98,99]
NF 3	Cotton				82.7–92	9.8	[98,100]
NF 4	Ramie	61.85–85	3–7.58	0.5–9.06	69–83	9	[72,73,74,77,78,96,97,98,101,102,103]
NF 5	Kenaf	2.2–6.2	9 8–21	20–33	31–72	9.2	[72,73,74,76,77,78,79,85,86,96,97,98,101,104]
NF 6	Bamboo	-	21–31	17.2–43.8	22.8–56.7	8.9	[72,73,74,77,78,79,96,97,98]
NF 7	Oil palm		24.45–29	19.06	47.91–65	11	[72,73,74,96,97,98]
NF 8	Flax	5–10	2–5	10.37–20.6	64.1–75	7	[72,73,74,76,77,78,79,85,86,98,99,101,105,106]
NF 9	Abaca	20–25	7–12.4	20–25	56–63	15	[72,73,74,76,77,78,79,85,96,97,98,100,101,105]
NF 10	Banana	11–12	5–10	10–24	60–65	12.1	[77,78,98,101]
NF 11	Jute	8	5–13	13–20.4	61–71	12	[72,73,74,76,77,78,79,85,86,96,97,98,101,104,105]
NF 12	Pineapple	5–12.7		18	70–82	13	[72,73,74,78,79,83,96,97,98]
NF 13	Sisal	10–25	8–14	10–38.2	60–78	11	[72,73,74,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,104,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121]
NF 14	Coir	30.45	40–45	0.15–0.25	32–43	10	[72,73,74,76,77,79,91,104,119,120,121,122,123,124,125]
NF 15	Coconut		8–13.1	4–20	70–77.6	8.2	[98,100,120,121,122,123,124]
NF 16	Sugar can bagasse		22.3–25.3	16.8–31.8	41.1–55.2	8.8	[72,73,74,97,98,99,125,126]

,

Table 6. The mechanical features of the plant fibers.

Code	Fiber Source	Elongation at Break (%)	Young’s Modulus (GPa)	Tensile Strength	Density (g/cm3)	References
NF 1	Rice	2.2	0.3–2.6	19–135	1.4	[72,77,78,79,126]
NF 2	Corn	3–4.7	10.1–16.3	355–580	1.2–1.4	[73,74,82,98,113,127,128,129,130,131,132,133]
NF 3	Cotton	3–10	5.5–12.6	45.5–1000	1.5–1.6	[73,74,82,98,112,127,128,129,130,131,132]
NF 4	Ramie	1.2–8	24.5–128	348–938	1.45–1.5	[72,74,82,98,112,127,128,129,130,131,132,133]
NF 5	Kenaf	1.6–6.9	2.86–60	215.4–1191	0.6–1.5	[72,73,74,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,127,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]
NF 6	Bamboo	1.5–11	11–17	140–230	0.6–11	[98,142]
NF 7	Oil palm	8–25	1–9	92–1200	0.7–1.55	[98]
NF 8	Flax	1.2–10	24–80	88–1600	0.6–1.5	[81,91,93,94,95,104,123,124,129,137,138,144,145]
NF 9	Abaca	3–10	3–12	220–980	1.5	[91,95,98,104,106,119,126,127,128,129,139,140,141]
NF 10	Banana	3–53	12–33.8	350–980	1.35	[91,95,98,104,106,119,126,127,128,129,139,140,141]
NF 11	Jute	1.16–8	10–55	385–850	1.3–1.5	[91,95,98,104,106,119,126,127,128,129,139,140,141]
NF 12	Pineapple	1–14.5	60–82	170–1672	0.8–1.6	[73,74,82,91,98,144,145,146,147]
NF 13	Sisal	2–25	9–38	80–840	1.3–1.5	[73,74,82,91,98,144,145,146,147]
NF 14	Coir	14.21–59.9	1.27–6	106–593	1.1–1.6	[73,74,82,91,98,144,145,146,147]
NF 15	Coconut	10–23	21.1	150	0.43
NF 16	Sugar can bagasse	1.1	17–27.1	20–290	1.2–1.5	[73,74,91,104,118,125,126,127,128,129,144]

, and

Table 7. The physical features of natural fibers [86,101,102,110,115,116,119,146].

Code	Fibers	Width of Lumen (micron)	Thickness of Single Cell Wall (micron)	Fiber Diameter (mm)	Fiber Length (mm)
NF 1	Rice	8.7	1.2	15.5	8.7
NF 2	Corn	20.1	1.4	26.7	20.1
NF 3	Cotton	16.4	56.0	45.0	16.4
NF 4	Ramie	13.0	60.4	80.0	13.0
NF 5	Kenaf (core)	22.7	1.1	37.0	22.7
NF 6	Bamboo	8.6	9.0	17.8	3.0
NF 7	Oil palm	9.8	11	25.0	1.4
NF 8	Flax	6.42	20.0	38.0	65.0
NF 9	Areca	18.1	1.2	476	60
NF 10	Banana	22.4	1.5	30.0	4.2
NF 11	Jute	7.6	11.3	30.0	6.0
NF 12	Pineapple	3	18.3	80.0	9.0
NF 13	Sisal	12.0	25.0	47.0	8.0
NF 14	Coir	21	0.06	12.0	0.3
NF 15	Coconut	3.2	8.0	14.0	1.0
NF 16	Sugar can bagasse	19.1	9.4	40.0	2.8

, respectively. The analysis included 16 different fibers with all properties obtained from the literature in accordance with the limits to be complied with for use in the aircraft cabin.

3.2. Results

This article discusses the most widely used matrices in biofiber-reinforced composites that are derived from renewable and petrochemical resources. This article includes an analysis of the lists of materials (natural fibers and polymers) obtained after extensive material research, including parameters of the properties of the materials that can be used for the aircraft cabin. This study presents two separate analyses for hybrid materials that can be obtained by using fiber materials obtained from renewable resources and polymers obtained from non-renewable resources for use in aircraft cabins. Non-renewable polymers were preferred, considering that materials used in the aviation industry should have higher values in terms of strength parameters. The first step involves developing the decision hierarchy involving mechanical, physical, and chemical features as the main criteria. The features listed in Table 5, Table 6 and Table 7 are added to the hierarchy as the sub-criteria. Appendix A in the Appendix illustrates the final hierarchy composed of main and sub-criteria. The application of Equations (1)–(10) results in the weights provided in

Table 8. Main criteria weights.

Criteria	%
Physical	16.98
Mechanical	44.29
Chemical	38.73

for the main criteria.

The next step involves a pairwise comparison of sub-criteria under each main criterion.

Table 9. Decision matrices.

	Micro-Fibrillar Angle	Lignin	Hemicellulose		Cellulose		Moisture Content	Normalized Principal Eigenvector
Chemical Features	1	2	3		4		5
Micro-fibrillar angle	1	1/3	1/3		1/4		1/2	7.92%
Lignin	3	1	1		1/2		1/2	16.02%
Hemicellulose	3	1	1		1/2		1/2	16.02%
Cellulose	4	2	2		1		1/3	24.69%
Moisture content	2	2	2		3		1	35.34%
Mechanical Features	Elongation at break	Young’s modulus		Tensile strength		Density		Normalized Principal Eigenvector
Elongation at break	1	2		3		1		36.32%
Young’s modulus	1/2	1		1/2		1/2		13.82%
Tensile strength	1/3	2		1		1/2		17.88%
Density	1	2		2		1		31.98%
Physical Features	Width of lumen	Thickness of single cell wall		Fiber diameter		Fiber length		Normalized Principal Eigenvector
Width of lumen	1	1/3		1/2		1/4		9.97%
Thickness of single cell wall	3	1		2		1		34.52%
Fiber diameter	2	1/2		1		1/2		18.50%
Fiber length	4	1		2		1		37.01%

presents the decision matrices using Saaty’s comparison scheme and the resulting weights.

Figure 1 illustrates the weights of the selected sub-criteria.

Sub-criteria weights are multiplied with their associated values given in Table 5, Table 6 and Table 7, followed by normalization of the resulting values due to the various scales. The top half of

Table 10. Weighted scores of natural fibers.

Normalized
	NF 1	NF 2	NF 3	NF 4	NF 5	NF 6	NF 7	NF 8	NF 9	NF 10	NF 11	NF 12	NF 13	NF 14	NF 15	NF 16
Micro-fibrillar Angle	0.062	0.062	0.062	0.224	0.013	0.062	0.062	0.023	0.069	0.035	0.025	0.027	0.054	0.093	0.062	0.062
Ligning	0.081	0.030	0.062	0.021	0.060	0.105	0.107	0.014	0.039	0.030	0.036	0.062	0.044	0.171	0.042	0.094
Hemicel.	0.073	0.144	0.062	0.016	0.083	0.094	0.060	0.047	0.070	0.053	0.052	0.056	0.075	0.001	0.038	0.076
Cellulose	0.042	0.044	0.091	0.080	0.054	0.041	0.059	0.073	0.062	0.066	0.069	0.080	0.072	0.039	0.077	0.050
Moisture Content	0.049	0.053	0.061	0.056	0.057	0.055	0.068	0.043	0.093	0.075	0.074	0.081	0.068	0.062	0.051	0.055
Elong.	0.013	0.023	0.039	0.027	0.025	0.037	0.098	0.033	0.039	0.166	0.053	0.046	0.080	0.218	0.098	0.007
Young’s Modulus	0.004	0.033	0.022	0.188	0.076	0.036	0.012	0.128	0.018	0.055	0.080	0.175	0.058	0.009	0.052	0.054
Tensile Strength	0.010	0.061	0.010	0.085	0.081	0.024	0.085	0.111	0.096	0.087	0.083	0.121	0.060	0.046	0.020	0.020
Density	0.057	0.053	0.063	0.060	0.042	0.235	0.045	0.042	0.061	0.055	0.057	0.049	0.057	0.055	0.017	0.055
Width of Lumen	0.041	0.095	0.077	0.061	0.107	0.041	0.046	0.030	0.085	0.106	0.036	0.014	0.057	0.099	0.015	0.090
Thickness	0.005	0.006	0.238	0.257	0.005	0.038	0.047	0.085	0.005	0.006	0.048	0.078	0.106	0.000	0.034	0.040
Fiber D.	0.015	0.026	0.044	0.079	0.036	0.018	0.025	0.037	0.469	0.030	0.030	0.079	0.046	0.012	0.014	0.039
Fiber Length	0.036	0.083	0.068	0.054	0.094	0.012	0.006	0.269	0.248	0.017	0.025	0.037	0.033	0.001	0.004	0.012
	Priorities
Micro-fibrillar Angle	0.005	0.005	0.005	0.018	0.001	0.005	0.005	0.002	0.005	0.003	0.002	0.002	0.004	0.007	0.005	0.005
Ligning	0.013	0.005	0.010	0.003	0.010	0.017	0.017	0.002	0.006	0.005	0.006	0.010	0.007	0.027	0.007	0.015
Hemicel.	0.012	0.023	0.010	0.003	0.013	0.015	0.010	0.008	0.011	0.009	0.008	0.009	0.012	0.000	0.006	0.012
Cellulose	0.010	0.011	0.023	0.020	0.013	0.010	0.015	0.018	0.015	0.016	0.017	0.020	0.018	0.010	0.019	0.012
Moisture Content	0.017	0.019	0.021	0.020	0.020	0.019	0.024	0.015	0.033	0.026	0.026	0.028	0.024	0.022	0.018	0.019
Elong.	0.005	0.008	0.014	0.010	0.009	0.013	0.036	0.012	0.014	0.060	0.019	0.017	0.029	0.079	0.036	0.002
Young’s Modulus	0.000	0.005	0.003	0.026	0.011	0.005	0.002	0.018	0.003	0.008	0.011	0.024	0.008	0.001	0.007	0.007
Tensile Strength	0.002	0.011	0.002	0.015	0.014	0.004	0.015	0.020	0.017	0.016	0.015	0.022	0.011	0.008	0.004	0.004
Density	0.018	0.017	0.020	0.019	0.014	0.075	0.014	0.014	0.019	0.017	0.018	0.016	0.018	0.017	0.006	0.017
Width of Lumen	0.004	0.009	0.008	0.006	0.011	0.004	0.005	0.003	0.009	0.011	0.004	0.001	0.006	0.010	0.002	0.009
Thickness	0.002	0.002	0.082	0.089	0.002	0.013	0.016	0.029	0.002	0.002	0.017	0.027	0.037	0.000	0.012	0.014
Fiber D.	0.003	0.005	0.008	0.015	0.007	0.003	0.005	0.007	0.087	0.005	0.005	0.015	0.009	0.002	0.003	0.007
Fiber Length	0.013	0.031	0.025	0.020	0.035	0.005	0.002	0.100	0.092	0.006	0.009	0.014	0.012	0.000	0.002	0.004
Total Weighted Score	0.104	0.150	0.231	0.263	0.159	0.189	0.165	0.247	0.313	0.185	0.157	0.204	0.195	0.185	0.124	0.129

provides the resulting normalized values. The final step of the methodology involves multiplying the weights of the main criteria with the associated normalized values under each sub-criteria. The bottom half of Table 10 provides the weighted score of each sub-criteria. Calculating the total scores for each natural fiber alternative shows the contribution of each alternative toward the overall goal of choosing the best natural fiber.

Total weighted scores are presented in the last row of Table 10, and Figure 2 shows that Areca is the optimum fiber type with a total weight score of wt = 0.313.

Table 11. The technical characteristics of polymers.

Polymer Code	Polymer Material	Elongation at Break (%)	Modulus of Elasticity (GPa)	Tensile Strength (MPa)	Density (g/cm3)	References
P 1	Vinyl ester resin	2	2–4.5	40–90	1.2–1.5	[145,146,147]
P 2	Polystyrene	1–3.6	1.2–2.6	35.9–56.6	1.04–1.06	[77,78,79,118]
P 3	Epoxy	1–6	3–6	35–100	1.1–1,4	[139,140]
P 4	Polybutylene terephthalate	250	1.93–3	50–60	1.30–1.38	[115]
P 5	Polyethylene terephthalate	30–300	2.76–4.14	48.3–72.4	1.29–1.40	[77,78,79,118]
P 6	Polycarbonate	70–150	2–2.44	60–72.4	1.14–1.21	[77,78,79,118]
P 7	Nylon 6	20–150	2.9	43–79	1.12–1.14	[145,146,147]
P 8	Polyamide	30–100	1.2–3.2	90–165	1.12–1.14	[77,78,79,118]
P 9	High density polyethylene (HDPE)	2.0–130	0.4–1.5	14.5–38	0.94–0.96	[145,146,147]
P 10	Low-density polyethylene (LDPE)	90–800	0.055–0.38	40–78	0.910–0.925	[145,146,147]
P 11	Acrylonitrile b utadiene styrene	1.5–100	1.1–2.9	27.6–55.2	1–1.2	[77,78,79,127]
P 12	PP	15–700	0.95–1.77	26–41.4	0.899–0.920	[145,146,147]

presents the characteristics of polymers commonly utilized for producing composite materials with natural fibers.

Applying the same steps in comparing the polymer alternatives results in the values provided in

Table 12. Normalized and weighted scores of polymer alternatives.

	Normalized
	P 1	P 2	P 3	P 4	P 5	P 6	P 7	P 8	P 9	P 10	P 11	P 12
Elongation at break	0.001	0.001	0.002	0.143	0.094	0.063	0.049	0.094	0.066	0.254	0.029	0.204
Young’s modulus	0.115	0.067	0.159	0.088	0.123	0.078	0.102	0.078	0.032	0.007	0.067	0.085
Tensile strength	0.092	0.064	0.092	0.078	0.085	0.094	0.087	0.181	0.038	0.084	0.058	0.048
Density	0.099	0.077	0.092	0.096	0.099	0.086	0.083	0.083	0.070	0.067	0.081	0.066
	Priorities
Elongation at break	0.000	0.000	0.001	0.052	0.034	0.023	0.018	0.034	0.024	0.092	0.010	0.074
Young’s modulus	0.016	0.009	0.022	0.012	0.017	0.011	0.014	0.011	0.004	0.001	0.009	0.012
Tensile strength	0.016	0.011	0.016	0.014	0.015	0.017	0.015	0.032	0.007	0.015	0.010	0.009
Density	0.032	0.025	0.029	0.031	0.032	0.028	0.027	0.027	0.022	0.021	0.026	0.021
Total weighted score	0.065	0.046	0.069	0.109	0.098	0.078	0.074	0.104	0.058	0.130	0.056	0.115

.

Total weighted scores are presented in the last row of Table 12, and Figure 3 shows that low-density polyethylene (LDPE) is the optimum polymer type with a total weight score of wt = 0.130.

The results of the multi-criteria decision-making approach employed in this study indicate that Areca fiber emerged as the best choice based on mechanical, chemical, and physical properties, particularly due to its strength-to-weight ratio and sustainability. Similarly, low-density polyethylene (LDPE) was identified as the most suitable polymer. Its high elongation, density, and modulus make it ideal for composite matrix applications in aviation, emphasizing durability and lightweight. Overall, the findings prioritize both eco-friendliness and structural integrity, with Areca and LDPE composites offering an optimal blend for aircraft cabin use.

4. Conclusions

The sectoral use of composite materials supported with polymer materials and containing biofibers has garnered significant attention in recent times. The usage of polymer materials as bindings for composites supported by biofibers has increased significantly.

In this study, a methodical process was followed in the most efficient natural fiber and polymer selection for use in aerospace implementations. First, this research presents a thorough investigation of the literature on the natural fibers and polymers that are most commonly used. To improve comprehension, information on the natural fibers’ chemical (micro-fibrillar angle, lignin, hemicellulose, cellulose, and moisture content), mechanical (elongation at break, Young’s modulus, tensile strength, and density), and physical (width of the lumen, fiber length, the thickness of single cell wall, and fiber diameter) characteristics and polymer’s technical characteristics (elongation at break, modulus of elasticity, tensile strength, and density) were gathered and examined prior to the selection process starting. For the purpose of this study, criteria and sub-criteria were assessed by experts. A hierarchical strategy methodology was performed to identify the weights assigned to the attributes based on the notional significance of each of the criteria. Using the process, the best variation in each fiber and polymer was chosen for use in applications. Based on the ranking and performance scores of these chosen polymers and fibers, the optimal substitute was evaluated.

Based on the chemical, mechanical, and physical attributes of natural fibers, Areca was evaluated to be the optimum material for the interior parts of an aircraft cabin while low-density polyethylene was determined to be the optimum alternative among other polymers based on the technical attributes of polymers that were considered within the scope of this study.

This analysis has the potential to help researchers, designers, and decision-makers in future research endeavors for aviation material applications. A potential direction for future research involves the assessment of animal or ceramic fibers as potential materials for various industrial applications. Another venue for future research involves the life-cycle analysis of the above-mentioned materials with the inclusion of financial feasibility measures.