Evaluation of Impact Energy Absorption by Natural Fiber Composites in Motorcycle Helmets

Abstract

In response to the growing concerns regarding motorcyclists’ safety and advancements in the motorcycle industry, this study investigated the potential of natural fibers as a sustainable approach for enhancing helmet protection, thus replacing the traditional use of expanded polystyrene. Utilizing statistical tools such as the Shapiro–Wilk test, Chauvenet’s criterion, and the interquartile range, we compared the impact energy absorption of composites reinforced with natural fibers, including sugarcane bagasse, coconut, and sisal, added to expanded polyurethane prototypes. The results, evaluated through confidence intervals, indicated that composites reinforced with 5% sugarcane bagasse, 5% and 10% coconut, and 10% and 15% sisal exhibited significantly superior impact absorption performance compared to pure expanded polyurethane. Composites with agave sisalana fibers exhibited low variability and reliable performance, with the 10% concentration showing the best results. Sugarcane bagasse fiber demonstrated strong stability, especially at a 5% concentration. Coconut fiber performed well at both 5% and 10% concentrations but showed the greatest variability among the fibers tested. These findings underscore the potential of natural fibers in enhancing helmet safety and suggest promising directions for future research into the ideal composite materials for motorcycle helmets, an inquiry that is currently underway.

1. Introduction

The motorcycle industry has experienced unprecedented growth, demonstrated by a significant increase in the motorization rate, which reflects the relationship between the number of registered vehicles and the population. Between 2000 and 2018, this rate surged by 456.2%, rising from 23.3 to 129.6 motorcycles per 100,000 inhabitants. By 2019, the motorization rate had increased to 134.1 [1].

With this expansion comes heightened concern regarding the safety of motorcycle riders. In this context, the motorcycle helmet serves as the primary protective equipment utilized by users of this mode of transportation. According to the Pan American Health Organization, wearing a helmet reduces the risk of death by 42% and the likelihood of major injuries by 69%. In 2022, the number of motorcyclist fatalities in crashes reached 6222, marking the highest figure ever recorded. These statistics underscore the helmet’s critical role in preventing fatalities and serious injuries in motorcycle accidents [2].

A motorcycle helmet is a vital piece of safety equipment composed of several components, including the shell, inner liner, comfort liner, chin strap, and visor, as illustrated in Figure 1 [3]. This investigation will focus on the inner lining, commonly called the protective padding. This component is typically made of expanded polystyrene (EPS) due to its soft, cushioning properties and robust structure designed to absorb impacts and minimize injuries [4].

However, technological advancements, growing global demand for resources, and evolving consumer desires and expectations have underscored significant material availability and environmental sustainability challenges. In this context, there is an increasing interest in natural fibers as innovative formulations, and techniques are continuously being explored, developed, and applied. This trend indicates that natural fibers are becoming more viable long-term alternatives capable of meeting market demands [5].

The impact of energy absorption by natural fiber composites has been studied in different areas, especially in ballistic science. These include proposals for examining the ballistic impact performance of natural-fiber-reinforced polymer composites designed for multilayer armor systems [6]. A second study aimed to investigate the fundamental characteristics of tucum fiber, focusing on its density, pullout properties, microstructural features, and its application in polymer matrix composites. The composites were subjected to tensile tests, as well as impact tests related to Izod and ballistic conditions [7]. Another relevant study aimed to assess the ballistic performance and energy absorption characteristics of composites reinforced with pineapple leaf fibers, using various conditions evaluated through residual velocity and Izod impact tests [8]. Moreover, finally, a study examined the incorporation of 10, 20, and 30 vol % caranan fiber into epoxy resin, focusing on the properties related to Izod notch toughness and ballistic performance [9]. Additionally, an Asian research group examined the mechanical properties of composites made from banana fiber and ramie fiber reinforced with silicon carbide (SiC) nanoparticles [10].

Furthermore, investigations have been conducted on fabricating a hybrid composite using chopped banyan fiber and sawdust cellulose reinforced with an epoxy polymer matrix through the hand layup. It aims to quantify the impact of the hybrid composite by assessing its mechanical properties and surface morphology [11].

Other areas of research include composites derived from polypropylene and reinforced with hemp fibers, which have been created to enhance the understanding of how this reinforcement affects impact strength and water uptake behavior [12].

Lastly, a natural lignocellulosic fiber was extracted from cow dung waste, and its suitability as a reinforcing material in resin-based polymer composites was assessed [13].

This research aims to compare the impact absorption performance of composites reinforced with natural fibers against expanded polyurethane (PU) foams. The novelty of this study lies in the potential to demonstrate that the protective padding of helmets can be made from PU with fibers instead of EPS, challenging existing paradigms in this area of the industry. To achieve this aim, the collected data underwent a comprehensive statistical analysis. Initially, a normality test was conducted to validate the data distribution, ensuring the appropriate application of statistical tools. This statistical approach is essential for guaranteeing the robustness and reliability of the obtained findings.

Statistical analysis played a crucial role in interpreting the collected data, facilitating an objective and grounded assessment of the impact absorption effects across the various tested materials. Consequently, applying statistical methods in this research provided a solid foundation for evaluating the data and drawing scientifically sound conclusions.

This study is organized as follows: This section highlights several key studies relevant to the topic. Section 2 describes the experimental methods for impact absorption, including the composite manufacturing process and the raw data collected. Section 3 outlines the statistical methods used to analyze the data. Section 4 applies the statistical tests to the data presented in Section 2 and discusses the results. Finally, Section 5 presents the conclusions, final thoughts on this study, and recommendations for future research.

2. Experimental Method for Impact Absorption

This section describes how the experiment was carried out and the results of the impact absorption test.

The experiments were conducted comparing only pure PU and not EPS, as EPS cannot be mixed with fibers, only layered.

The choice of coconut, sisal, and sugarcane fibers, derived from Brazil’s vast biodiversity, illustrates the efficient use of the country’s renewable natural resources. Furthermore, the incorporation of these fibers into polymeric composites aimed to improve mechanical characteristics, such as impact resistance, to promote the alignment of materials technology with the principles of environmental sustainability [14,15,16].

Coconut fiber is characterized by its mechanical strength and toughness, properties that stem from its distinctive fibrous nature. Its ability to absorb a significant amount of energy before failure, attributed to its unique microscopic structure and lignocellulosic composition, emphasizes the potential of this fiber in improving the performance of reinforced composites, aligning technological innovation with environmental sustainability [17]. The integration of these fibers in various industrial sectors reveals their potential as a viable alternative to synthetic materials, offering a solution that combines superior mechanical strength, lightweight, and biodegradability.

The sisal plant, also known as Agave sisalana, is a predominant species in tropical regions with a global distribution. The fibers obtained from the leaves of this plant are known for their remarkable heterogeneity in their physical–chemical and mechanical properties, such as diameter, length, and tensile strength, which can vary significantly even within a single leaf. However, the use of sisal fiber in composites faces challenges related to its propensity for moisture absorption. This factor can lead to fiber swelling, negatively affecting the mechanical strength and durability of the composites. Therefore, to optimize the performance of sisal fiber as reinforcement, surface treatment, such as alkalization, is essential for promoting compatibility and adhesion between the fiber and the polymer matrix, ensuring a more effective interfacial interaction [18,19].

Sugarcane bagasse fibers are intrinsically hydrophilic, meaning they absorb moisture from the environment, resulting in swelling and deterioration of their mechanical properties. To mitigate such issues, various chemical modification techniques have been explored. Processes such as alkalization, treatment with potassium permanganate, acetylation, silanization, benzoylation, treatment with acetone, and acrylation alter the polar groups of the fibers, imparting hydrophobic characteristics and improving their compatibility with polymer matrices. These modifications facilitate the formation of more stable and durable composites, as documented in the scientific literature, highlighting them as effective strategies for overcoming the integration barriers of natural fibers in engineering applications [20].

Recent advances in PU processing methods allow for the production of forms with varying densities and rigidities, making PU valuable in applications that require materials with impact resistance, hardness, and elasticity. Furthermore, its properties of moisture, wear, and corrosion resistance solidify its position as a highly relevant research material in the field of advanced composites [21]. The versatility of PU is amplified by its ability to integrate additives and fillers, which enhance its mechanical and thermal properties through interfacial interactions between the fibers and the polymer matrix. This capability grants PU extensive applicability across various industrial sectors, benefiting from its viscoelasticity, which provides rigidity while allowing for energy dissipation through frictional losses. Additionally, the use of natural fillers can promote the production of more sustainable PU foams, maintaining their mechanical properties so they remain intact and enhancing the sustainability of industrial processes [22].

The main mechanical properties of these fibers and PU are available in

Table 1. Main mechanical properties of the fibers and PU.

Fiber/Foam	Tenacity	Linear Density	Tensile Strength	E-Modulus
Bagasse sugarcane	7.5–22 cN/Tex [23]	18.72–54.2 Tex [23]	290 MPa [24]	17 GPa [24]
Sisal	36–44 cN/Tex [23]	28.6–48.6 Tex [23]	100–1500 MPa [25]	9.4–28 GPa [25]
Coconut	8–18 cN/Tex [23]	50 Tex [23]	131–220 MPa [25]	4 to 6 GPa [24]
PU	Not available *	30–300 kg/m3 [26]	0.2–3 MPa [26]	7–200 MPa [26]

* Property not determined for foams.

:

The coconut and sisal fibers were purchased at a local market, while the sugarcane fiber was obtained as a donation from a market vendor. All fibers were treated before being added to the PU.

The sisal fiber used in this study is composed of 100% natural sisal, as specified by the manufacturer. The material is sold in 100 g and 250 g packages, with the universal product barcode 789761352052-9. According to the manufacturer’s specifications, the fiber is indicated for general heavy cleaning and removing excess grout from floors and tiles; it is also intended for applications in civil construction, such as fixing plaster.

The coconut fiber used in this study is a 100% natural, renewable, and sustainable product, recognized for its lightness. In addition, coconut fiber is biodegradable, acts as a natural fungicide, and is suitable for both landscaping and decoration, contributing to the preservation of the environment. The product used was marketed by Premium West Garden (SKU 1039), with barcode 789648881039. The packaging has the following dimensions: a width of 29 cm, height of 40 cm and length of 10 cm, with a total weight of 200 g. The composition of the material is exclusively 100% natural coconut fiber. This material is widely used in gardening due to its ability to maintain adequate levels of moisture, while providing good drainage and aeration.

2.1. The Manufacture of Composites

The production of polyurethane (PU) foams was conducted using a 1:1 mass ratio of polyol to isocyanate, achieving an expansion rate of 20 times the initial volume and a density of 1.3 g/cm³, as specified by the manufacturer (AVIPOL^®). Specific natural fibers—coconut, sugarcane bagasse, and sisal—were incorporated during the production process. These fibers were chosen for their mechanical properties, widespread availability in Brazil, and their frequent improper disposal as solid waste, thereby highlighting an opportunity for sustainable utilization [27].

All fibers used in this study originate from the state of Rio de Janeiro, Brazil, and were sourced from local suppliers, without being tied to a specific brand or manufacturer. The natural fibers underwent an alkaline treatment to improve their affinity and adhesion to the polymeric matrix and remove lignin, waxy substances, and other surface impurities before their incorporation into the PU. This treatment was performed in a 10% sodium hydroxide solution (PA, VETEC®) with continuous agitation at 1300 rpm for 15 min in a controlled-temperature environment (25 °C ± 5 °C). After treatment, the fibers were rinsed with distilled water until a neutral pH was achieved, then dried in an oven at 70 °C for 24 h to ensure complete moisture removal. The fibers were subsequently ground using a Wiley-type knife mill equipped with four blades and sieved with an electromagnetic sieve shaker featuring a 10 mm mesh to ensure particle size uniformity, Figure 2 [28,29].

To prepare the test specimens, it was necessary to calculate the polyvinyl chloride mold’s volume, considering the PU’s expansion. The cylindrical mold, with a diameter of 10 cm and thickness of 1 cm, had its volume calculated using the formula $V=\pi ·{\left({\displaystyle \frac{d}{2}}\right)}^2·h,$ resulting in 78.54 cm³. After division by the PU expansion factor, an initial volume of 3.93 cm³ was obtained, corresponding to 5.11 g of material for the formulation. The composites were prepared with 5%, 10%, and 15% fiber proportions, limited experimentally to 15% due to restrictions identified in higher fiber volumes as reported by de Paula et al. [30], due to uncontrolled expansion of the polymer. This standardization ensured consistency and efficacy in fabricating the test specimens.

All stages of the composite fabrication were conducted in a controlled-temperature environment (25 °C ± 5 °C), adhering to a total curing time of 48 h as recommended by the manufacturer prior to the impact absorption tests. The testing was performed at the Product Testing Laboratory (Laboratório de Ensaios em Produtos) (LAENP) of the National Institute of Technology in Brazil. To assess the variability of the results, ten replicates of each composite were produced, except for the 15% sisal composite, for which thirty replicates were formulated based on preliminary statistical analysis.

2.2. Impact Absorption Test

The gravitational acceleration exerted on the test head, along with the helmet, during a guided free-fall test, was used to assess the helmet’s impact absorption capabilities. During the test, the helmet assembly was dropped from specific heights, achieving an impact speed of (7.00 ± 0.15) m/s on a flat surface and (6.00 ± 0.15) m/s on a hemispheric surface. The ABNT standard NBR 7471:2015 regulates the maximum allowable acceleration to ensure user safety. Notably, this maximum acceleration is expressed in multiples of “g”, where “g” is equivalent to 9.80665 m/s². The regulations stipulate that acceleration must not exceed 300 “g” at any point and must not surpass 150 “g” for more than 5 ms [31].

The ABNT NBR 7471:2015 standard aligns closely with the testing principles outlined in UNECE Regulation 22.06 [32], which defines uniform requirements for the approval of protective helmets and visors for motorbike and moped users. Both standards assess the helmet’s impact absorption capacity using a guided free-fall test that measures the gravitational acceleration exerted on a test head equipped with the helmet. However, each standard incorporates adaptations to address the specific conditions and regulatory requirements of its respective region.

The absorbed energy was calculated following the procedures outlined in ECE Regulation No. 22.05 [33]. The acceptance criteria, originally established in ECE Regulation No. 22.05 and later incorporated into the NBR standard, include a maximum resultant acceleration of 275 g (ECE) or 300 g (NBR) and a maximum HIC value of 2400, which is exclusive to the ECE criteria.

During the impact absorption tests, the absorbed energy was evaluated based on the acceleration data recorded at the center of gravity of the head form during the impact. The assessment included the calculation of the Head Injury Criterion (HIC) using Equation (1):

$$HIC={\max }_{t_1,t_2}\left(\left(t_2-t_1\right){\left[{\displaystyle \frac{1}{t_2-t_1}}{\int }_{t_1}^{t_2}a\left(t\right)\hspace{0.17em}dt\right]}^{2.5}\right)$$

(1)

where $a\left(t\right)$ represents the acceleration measured in multiples of the acceleration of gravity (g) and $t_1 \mathrm{and} t_2$ represent the time limits used for the calculation. The results comply with the acceptance criteria established by the standards. This approach ensures both the standardization and reliability of the impact test results.

The experimental tests were conducted using a vertical impact testing machine, model MAU 1006, manufactured by AD Engineering, located in Bergamo, Italy. Figure 3 shows that this equipment is equipped with an integrated computer system that automatically records test results, including the acceleration of helmet head assembly, expressed in units of g (9.81 m/s²), and the impact duration in milliseconds (ms). The testing machine handles the entire measurement and processing steps, including the application of Equation (1), and yields the acceleration value corresponding to the HIC in units of g.

In this procedure, a test head with a circumference of 62 cm and a mass of 6.1 kg (±0.18 kg) was employed, chosen for its larger size and mass to simulate more severe impact conditions and represent a critical scenario for evaluating impact energy absorption. This impact head is equipped with a triaxial piezoelectric accelerometer positioned at its center of gravity, featuring an integrated amplifier and power source, along with three low-pass filters that have a cutoff frequency of 2 kHz and a nominal sensitivity of 1 mV/(m/s²), as illustrated in Figure 4.

The test was conducted from a standard height of 50 cm, commonly utilized in interlaboratory comparisons. The drop begins from point P, located at the upper rear of the test head. This area is of utmost importance because it is a location that might be severely damaged during testing. By choosing point P, we can be sure that the assessment accurately depicts the helmet’s energy absorption capabilities under extreme conditions, in line with the stringent safety requirements of the testing protocol. The final impact velocity calculated using the formula $v=\sqrt{2gh}$ reached 3.13 m/s, where g represents the gravitational acceleration (9.81 m/s²) and h is the height in meters (0.50 m). The impact occurred on a flat steel surface with a diameter of 130 mm (±3 mm), a standard used in motorcycle helmet testing.

To ensure appropriate parameters for statistical analysis, the composites were subjected to impact testing in a randomized order. Each composite was placed on the standard flat base of the MAU 1006 testing machine, and the machine’s integrated computer system released the test head in a controlled manner. Due to the destructive nature of the test, each sample experienced a single impact at point P, ensuring that each test accurately reflected the impact absorption capacity of the composite (Figure 5).

After the tests, the results were collected and organized; these are presented in

Table 2. Impact absorption test results.

Free Fall (g)
0%	5%			10%			15%
Pure PU	Sugarcane Bagasse	Coconut	Agave Sisalana	Sugarcane Bagasse	Coconut	Agave Sisalana	Sugarcane Bagasse	Coconut	Agave Sisalana
65.99	51.86	52.19	68.02	64.12	48.70	53.82	65.36	66.45	68.22	58.33	62.97
65.30	61.72	64.27	69.50	58.81	64.74	75.40	62.66	63.81	56.90	49.84	56.60
56.72	59.04	54.94	65.28	58.00	58.44	56.12	67.15	71.30	59.72	71.82	74.92
65.47	58.41	61.16	70.08	62.77	63.93	62.24	71.70	61.55	71.68	83.20	57.35
76.79	59.86	58.73	63.10	65.99	65.53	55.24	64.87	77.42	60.70	63.78	58.82
86.44	64.61	55.47	80.45	60.14	60.11	57.41	61.42	75.61	77.52	64.17	65.42
65.72	66.71	61.81	63.86	67.00	58.65	62.12	63.07	57.08	54.73	67.39	57.41
78.86	60.81	49.59	61.28	62.00	59.49	59.87	61.74	66.72	54.78	64.78	52.19
78.50	58.26	59.13	61.28	69.22	54.78	61.05	57.90	57.11	52.35	59.62	68.14
73.78	56.35	61.54	68.11	63.29	54.23	56.85	96.53	65.24	53.72	65.65	58.10

, which summarizes each evaluated composite’s impact absorption test results.

For composites with 15% sisal fiber, 30 samples were tested. This number was selected to enable a preliminary statistical analysis of the data behavior, ensuring greater reliability and robustness of the results.

The measurement uncertainty is 3.6% for impact absorption tests, at a 95% level of confidence with a coverage factor k = 1.96.

After the tests, the specimens were recycled for further use.

The data presented in Table 2 indicate that the samples analyzed in this study are independent and unpaired, which is a crucial factor in determining the most appropriate statistical method. In this context, data normality was assessed to guide the selection of a parametric or nonparametric analysis method, thereby ensuring a rigorous interpretation of the results.

3. Statistical Methodologies

The methodologies for statistically analyzing the impact energy absorption test findings are discussed in this section. The steps needed to verify the data’s normality and the tests used to identify any outliers are discussed. This part is critical in understanding the methodologies used for data analysis, allowing for a thorough and correct review of the results collected.

3.1. Shapiro–Wilk Test

The Shapiro–Wilk test, introduced in 1965, stands out among the different statistical procedures for testing the normality of data, particularly when working with sample sizes of less than fifty [34]. Because of its usefulness, this test has become extensively recognized and employed, garnering recognition for its robustness and strong statistical power qualities. When one examines an ordered random sample, indicated by x₁ ≤ x₂ ≤ ⋯ ≤ x_n, the statistics associated with the Shapiro–Wilk test are expressed as follows:

$$W={\displaystyle \frac{({\sum }_{i=1}^{n}aixi{)}^2}{\sum (xi-\overline{x}{)}^2}}$$

(2)

The i-third statistical order is represented by $xi$ in this formula, and the sample average is represented by $\overline{x}$. Furthermore, the sequence is defined as $a-\left(a1,\dots ,an\right)-{\displaystyle \frac{m^T{V}^{-1}}{{(m}^T{V}^{-1}m{)}^{1/2}}}$ where m denotes the expected values of order statistics obtained from independent random variables and distributed identically according to the standard normal distribution. The matrix V, in turn, corresponds to the order statistics’ covariance matrix. This metric is effective in detecting normalcy deviations across a variety of datasets [35].

3.2. Parametric Test for Outliers: Chauvenet’s Criterion

The employment of Chauvenet’s criterion is a frequently used statistical strategy for identifying outliers in datasets. Based on this criterion, a measure should be rejected if $\left|dj\right|=\left|y_j-\overline{y}\right|>d_{ch}$, where $d_{ch}$ is Chauvenet’s limit, defined as follows:

$$Po={\int }_{-\mathrm{\infty }}^{{-d}_{ch}}G\left(n\right)d_n+{\int }_{{+d}_{ch}}^{+\mathrm{\infty }}G\left(n\right)d_n={\int }_{-d_{ch}}^{{+d}_{ch}}G\left(n\right)d_n={\displaystyle \frac{1}{2n}}$$

(3)

The integral of the Gaussian function in two symmetrical intervals yields the probability, Po. Simplifying the integral yields the formula Po = 1/(2n), where n is the number of samples. In other words, a measurement can be ruled out if the likelihood of obtaining a deviation as large or greater than the observed deviation from the average is less than 1/(2n). This criterion states that, if the r value determined by $r={\displaystyle \frac{|y_i-\overline{y}|}{S\left(X\right)}}$, is greater than the critical value for the relevant degrees of freedom, a measure, y_i, should be eliminated [36].

3.3. A Nonparametric Test for Outliers: Interquartile Range (IQR)

We chose a technique based on interquartile range (IQR) within the framework of statistical analysis to identify outliers. This differs from traditional practice, which frequently employs standard deviation in parametric testing. Based on quartile principles, this approach identifies outliers as points that exceed 1.5 times the IQR, either below or above this limit.

The IQR is defined as the difference between the third and first quartiles, which correspond to the 75th and 25th percentiles, respectively. As a result, when one employs this technique, data that fall outside the bounds provided by Equations (4) and (5) are labeled as potential outliers:

$$Q_1-1.5\times \left(Q3-Q1\right)$$

(4)

$$Q_3+1.5\times \left(Q3-Q1\right)$$

(5)

In this context, it is vital to emphasize the impact of x, a multiplicative factor that influences sensitivity in outlier detection. This criterion was chosen to find locations that depart significantly from the center trend of data distribution, indicating potential contradictions that should be investigated further [37].

4. Results and Discussion

The data obtained from the impact energy absorption test were subjected to the Shapiro–Wilk test to assess the normality of the polyurethane foam (PU) and the composites reinforced with natural fiber prototypes. Hypothesis testing was conducted using R-Studio, a widely used tool for statistical analyses. The p-values obtained were considered to evaluate the normality of the distribution; a distribution was deemed normal when the p-value exceeded 0.05.

The following hypotheses were formulated to assess the data distributions: H0: there is statistical evidence that the data are normally distributed; H1: there is no statistical evidence that the data are normally distributed.

Table 3. Shapiro–Wilk for free-fall results.

Type and Quantity of Fiber	p-Value	Conclusion
PU 0%	0.575	There is no evidence to reject the null hypothesis
Sugarcane bagasse 5%	0.929	There is no evidence to reject the null hypothesis
Coconut 5%	0.655	There is no evidence to reject the null hypothesis
Agave sisalana 5%	0.086	There is no evidence to reject the null hypothesis
Sugarcane bagasse 10%	0.940	There is no evidence to reject the null hypothesis
Coconut 10%	0.605	There is no evidence to reject the null hypothesis
Agave sisalana 10%	0.018	One has evidence to reject the null hypothesis
Sugarcane bagasse 15%	0.238	There is no evidence to reject the null hypothesis
Coconut 15%	0.588	There is no evidence to reject the null hypothesis
Agave sisalana 15%	0.238	There is no evidence to reject the null hypothesis

presents the test results, which provide insights into the adequacy of the data distribution concerning the normality assumption.

The results presented in Table 3 were evaluated at a 5% significance level. The null hypothesis, H0, was rejected for the 10% sisal dataset, in bold, indicating that it does not follow a normal distribution. In contrast, the null hypothesis was not rejected for the other datasets, suggesting they may approximate a normal distribution.

Based on these findings, outliers were addressed. Chauvenet’s criterion was applied to the datasets that exhibited a normal distribution. At the same time, the interquartile range (IQR) method was used for the dataset where the null hypothesis was rejected. The outcomes of these analyses are shown in

Table 4. Chauvenet’s test for free-fall results.

	Free Fall (g)
	0%	5%			10%		15%
	Pure PU	Sugarcane Bagasse	Coconut	Agave Sisalana	Sugarcane Bagasse	Coconut	Sugarcane Bagasse	Coconut	Agave Sisalana
Mean	71.36	59.76	57.88	67.10	63.13	58.86	67.24	66.23	62.36
Standard deviation	8.92	4.16	4.69	5.70	3.60	5.25	10.94	6.95	7.99
Outlier	-	-	-	80.45	-	-	-	-	83.20

and

Table 5. The interquartile range for free-fall results.

10% Agave Sisalana (g)
1st quartile (Q1)	56.30
3rd quartile (Q3)	61.85
IQR (Q3-Q1)	5.55
Upper limit $Q_3+1.5\times \left(Q3-Q1\right)$	70.18
Lower limit $Q_1-1.5\times \left(Q3-Q1\right)$	47.98
Outlier	75.40

.

According to the results presented in Table 4, applying Chauvenet’s criterion revealed outliers in the sisal sample sets with 5% and 15% concentrations. These outliers were excluded from the analysis, as their presence could negatively impact the results and hinder the accurate interpretation of the data.

The interquartile range (IQR), defined as the difference between the third quartile (Q3) and the first quartile (Q1), serves as a measure of dispersion for the central half of the observed data. One used IQR to establish lower and upper thresholds for effective outlier detection. As outlined in Table 5, an outlier was identified in the dataset containing 10% Agave sisalana, with a value of 75.40. This atypical value was promptly excluded from subsequent analyses to ensure accurate interpretation and sound conclusions regarding the results.

After this outlier was excluded, the modified dataset underwent the Shapiro–Wilk test to evaluate its normality. This step was undertaken due to concerns that the outlier might have distorted our understanding of the data’s distribution. One employed a 5% significance level for this test, and the hypotheses evaluated were as follows: H0: there is statistical evidence that the data are normally distributed; H1: there is no statistical evidence that the data are normally distributed. The results of this test are presented in

Table 6. Shapiro–Wilk: 10% agave sisalana (without outliers).

Type and Quantity of Fiber	p-Value	Conclusion
Agave sisalana 10%	0.9245	There is no evidence to reject the null hypothesis

.

The analyses confirmed that all datasets conform to a normal distribution. One of the analyses employed the confidence interval method at a 5% significance level in comparing the various composites to the polyurethane prototype. This method enabled the assessment of the accuracy of the sample means. Since several samples contained fewer than 30 observations and assumed a normal distribution, we utilized Student’s t-distribution to determine the critical values. The standard error (SE) was computed from the sample standard deviation, minimized by the number of samples [38], as described by the following formula: $SE=\sigma /\sqrt{n}$. The expression $\overline{x}\pm t\times SE$ defines the CI.

Table 7. Confidence interval results for the different prototypes.

Prototype	Mean	Standard Deviation	Samples	Confidence Interval
PU 0%	71.36	8.92	10	6.38
Sugarcane bagasse 5%	59.76	4.16	10	2.97
Coconut 5%	57.88	4.69	10	3.36
Agave sisalana 5%	65.61	3.43	9	2.64
Sugarcane bagasse 10%	63.13	3.59	10	2.57
Coconut 10%	58.86	5.25	10	3.76
Agave sisalana 10%	58.30	3.11	9	2.39
Sugarcane bagasse 15%	63.99	3.93	9	3.02
Coconut 15%	66.23	6.95	10	4.97
Agave sisalana 15%	61.64	7.07	29	2.69

presents the results for each dataset.

Figure 6 illustrates the confidence intervals for each composite group, enabling a visual comparison of the variations between the materials under investigation.

The analysis revealed a significant difference in performance between the pure polyurethane (PU) prototype and the composites containing varying proportions of natural fibers. As illustrated in Figure 6, pure PU exhibited both the highest mean performance, and the most significant variability compared to the fiber-reinforced composites. This result suggests that the incorporation of natural fibers enhanced the composites’ performance relative to pure PU, indicating that their addition positively affected the properties and efficiency of the composites, thereby highlighting their potential as a promising alternative to the reference material.

Those containing agave sisalana fibers displayed the lowest variability among the reinforced composites, indicating a consistent and predictable response. Notably, the 10% agave sisalana proportion achieved the best performance among the samples, suggesting that this concentration is particularly advantageous for this fiber. The 5% and 15% proportions also demonstrated satisfactory performance, reinforcing the potential of sisal as an effective reinforcement for PU composites.

Sugarcane bagasse fiber exhibited good stability, particularly at the 5% proportion. Although higher concentrations performed within acceptable limits, the results suggest that larger proportions may reduce energy absorption efficiency. This finding indicates that lower concentrations are more suitable for optimizing the performance of composites with this fiber.

Coconut fiber yielded good results at the 5% and 10% proportions; however, it exhibited the highest variability among all the tested fibers. Furthermore, the composite with 15% coconut fiber showed the poorest results in terms of both variability and effectiveness, suggesting that lower concentrations are preferable for achieving more efficient performance.

5. Conclusions

This study evaluated the effect of an impact absorption test on composites reinforced with natural fibers. Based on the data obtained, one can conclude that there are statistically significant differences in impact absorption between pure polyurethane (PU) prototypes and composites enhanced with natural fibers. Furthermore, this study highlights that including natural fibers improves impact absorption performance, corroborating previous research findings.

The results confirm that natural fibers, such as coconut, sugarcane bagasse, and sisal, are effective reinforcements in polymers, enhancing the impact absorption capabilities of the composites. It was observed that sugarcane bagasse and coconut fibers performed better at lower proportions, while sisal fiber demonstrated greater effectiveness at an intermediate proportion.

The analysis further quantified the effect of filler content, showing that sugarcane bagasse and coconut fibers performed better at lower concentrations (5–10%), while sisal fiber demonstrated greater effectiveness at an intermediate proportion (10%).

These findings contribute to the body of knowledge on natural-fiber-reinforced composites and their application in enhancing impact absorption for protective equipment, such as motorcycle helmets. The results emphasize the importance of producing composites with varying proportions and types of natural fibers to achieve optimal impact absorption performance.

However, it is crucial to note that further research is needed to expand knowledge in this area. Future studies could explore additional aspects of these composites, such as mechanical strength and durability, as well as the effects of processing and the overall characteristics of the materials, besides comparing the impact absorption properties of natural-fiber-reinforced composites with polystyrene. This analysis can highlight the reasons why one type of fiber is better than another. Furthermore, such investigations would provide a more comprehensive and in-depth understanding of natural-fiber-reinforced composites and their applications in protective equipment. Another promising direction for future work is the development of a biodegradable foam matrix reinforced with natural fibers. In addition, we recommend estimating the cost of the PU pad or helmet with the addition of the individual natural additives mentioned, as well as the cost of the pad without these additives. Additionally, it would be useful to analyze how much the inclusion of natural fibers increases the cost of the helmet or helmet pad and analyze the failure status of the helmets in different configurations.