Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
ISSN 2580-0817
Multiple InputAeSingle Output (MISO) Framework for Low Velocity Impact Response of Hybrid Gongronema latifolium/S-Glass Fibre Epoxy Composites Christian Emeka Okafor1,2.
Peter Chukwuemeka Ugwu2,3.
Godspower Onyekachukwu Ekwueme4*.
Nyrettin Akyakale5.
Emmanuel Chukwudi Nwanna1 TETFund Centre of Excellence for BioMedical.
Engineering and Agricultural Translational Studies (TCE-BEATS).
Nnamdi Azikiwe University.
Awka.
Anambra State.
Nigeria.
Department of Mechanical Engineering.
Nnamdi Azikiwe University Awka.
Anambra State.
Nigeria.
Department of Power Engineering Technology.
Nova Scotia Community College.
Akeyley Campus.
Dartmouth.
Canada.
Department of Industrial and Production Engineering.
Faculty of Engineering.
Nnamdi Azikiwe University Awka.
Anambra State.
Nigeria.
Textile.
Clothing.
Footwear and Leather Department.
Gerede Vocational School of Higher Education.
Abant zzet Baysal University.
Bolu 14900.
Turkey *Corresponding author: og.
ekwueme@unizik.
Article history:
Received: 15 May 2025 / Received in revised form: 1 June 2025 / Accepted: 6 June 2025
Available online 14 June 2025
ABSTRACT
Sustainable composites are vital for impact-critical aerospace, automotive, and defense applications.
This study used Multiple InputAeSingle Output (MISO) experimental approach to assess how hybrid ratio, mass fraction, and fiber orientation influence the low-velocity impact behavior of Gongronema/S-glass epoxy Gongronema fibers and S-glass were combined with ER-F292 epoxy and molded into ASTMstandard samples.
Charpy impact tests measured energy absorption.
A 60-run design evaluated input variable combinations, and Multiple Linear Regression identified significant predictors using p-values and confidence intervals.
Results showed that the mean values for hybridization ratio, mass fraction, fiber orientation, and low velocity impact were .
, .
79%), .
90A), and .
82 J), respectively.
It was found that the mass fraction had significant negative correlation with low velocity impact .
= -0.
p = 0.
as did the fiber orientation .
= -0.
p = 0.
The results for R = .
RA = .
F = .
and Durbin-Watson = .
show that the regression model is highly predictive.
Regression coefficients indicated negative effects from hybridization ratio (-0.
, mass fraction (-0.
, and fiber orientation .
, all statistically significant .
= 0.
Residual plots confirmed model validity.
The TEM images of confirmation test sample 1 reveal fiber-matrix interfaces with particle sizes between 10.
02Ae26.
40 nm.
Variations in scale .
nm and 50 n.
show microstructural differences, suggesting strong adhesion, dispersion aggregation, and anisotropic behavior due to 90-degree fiber orientation within epoxy matrix.
The study concludes that strategic optimization of input parameters significantly enhances the impact resistance of hybrid biocomposites.
Copyright A 2025.
Journal of Mechanical Engineering Science and Technology.
Keywords: Fiber orientation.
Gongronema latifolium, hybrid composites, hybridization ratio, low velocity impact, mass fraction.
MISO framework.
Introduction The increase in focus on hybrid fibre-reinforced polymer composites is due to the need for lightweight, eco-friendly, tough materials in structural design.
Low velocity impact (LVI) is a crucial issue, as it can result in serious accidents if energy from impact is not absorbed well by materials in the aerospace and automotive fields.
Low Velocity Impact DOI: 10.
17977/um016v9i12025p177 Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
ISSN 2580-0817
(LVI) refers to a type of impact event where an object strikes a material or structure at relatively low speeds, typically below 10 m/s.
It often causes internal damage, such as delamination or matrix cracking, especially in composite materials, without visible surface deformation or penetration .
Combining natural fibres and Gongronema latifolium with S-glass provided ways to improve composites and make them more economical and environmentally friendly.
The use of natural fibres such as plantain fibres .
, miscanthus fibres .
, .
, banana-coir fibre .
, and Dioscorea alata stem fibres .
, .
in reinforced composite development has been extensive.
However, limited studies are available on using G.
latifolium fibres in this context.
Despite considerable advancements in the development of fibre-reinforced composites, the impact performance of natural-synthetic hybrid composites remains inadequately understood, particularly in configurations involving G.
latifolium fibres.
Moreover, the challenge lies in modelling the complex interrelationships between multiple influencing factors such as hybridisation ratio, fibre mass fraction, orientation, and a single mechanical output .
ow velocity impact energ.
Existing single-variable studies cannot accurately anticipate such behaviour, so a more advanced statistical system, such as MISO, is It highlights the importance of having a good method that captures the different ways structural parameters affect LVI .
Using statistical and computational methods, the MISO framework helps you examine the relationship between a single output and several MISO works well in the field of composite impact analysis since parameters such as fiber volume fraction, fiber direction, and the ratio of different fibers have a collective impact on mechanical impact resistance .
Several studies have resorted to MISO-type modelling to study how mechanical properties change in hybrid composites.
For example.
Hiremath et al.
revealed that how materials are layered and the types of fibres used considerably impact how much energy they can absorb during low velocity impacts.
Mohammed et al.
also pointed out that increasing the bonding between the matrix and fibres and integrating various fibre types can boost energy dissipation in composites.
They show that combining various ideas to describe a crucial result is important.
The MISO framework becomes indispensable when used in G.
latifolium/S-glass hybrid epoxy composites.
Because G.
latifolium fibres are biodegradable and not very strong, they should be treated with chemicals and combined with S-glass to handle the structural demands.
The combination offers a synergy where the natural fibreAos ductility and toughness complement the glass fibreAos high stiffness and impact strength .
However, the impact behaviour is contingent upon the interplay of structural parameters, necessitating a modelling framework that can holistically account for such dependencies.
Multiple regression analysis is integral to the MISO framework, particularly in experimental mechanics.
This statistical method, known as multiple regression, is part of the larger design of experiment family and can measure how each input changes the output, adjusting for the effects of others .
, .
Modelling these composites helps adjust the mix and layout of their ingredients to favour impact resistance and stay eco-friendly and The Multiple InputAeSingle Output (MISO) framework relies on multiple regression analysis to determine how many factors impact one outcome together.
This analysis reveals both the relationships between factors and which factors are most important.
If changes in fibre orientation improve the performance more than variations in hybrid design, designers will know where to target their improvements.
Multiple regression also builds confidence in predictions through statistical measures like the coefficient of determination (RA) and significance levels.
These checks ensure that the model fits well and the results are trustworthy.
In the MISO framework, multiple regression is expected to Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
ISSN: 2580-0817 Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
transform complex data into a clear, actionable understanding, helping engineers develop stronger, more reliable composites for real-world use.
The need for the present study stems from the growing demand for sustainable, highperformance materials in engineering applications.
Combining natural and synthetic fibres, hybrid composites offer promising mechanical properties and environmental.
However, understanding how multiple fabrication parameters collectively influence impact resistance remains challenging.
Most existing research focuses on single-factor effects or uses simplistic models, limiting the ability to optimise composite design comprehensively.
Apart from recent studies .
, the G.
latifolium fibre, a locally sourced natural fibre, has shown potential as reinforcement but lacks a detailed study within hybrid systems under impact loading.
Furthermore, while S-glass fibres are well-known for their strength, the interaction effects between hybridisation ratio, fibre orientation, and mass fraction on LVI have not been systematically quantified.
Previous studies often neglect such multifactorial influences or rely on trial-and-error approaches, resulting in suboptimal composite performance.
Using the MISO framework with multiple regression analysis helps to sort out these gaps in a structured way.
It allows us to study the effects of various inputs on a single output at once, which helps us grasp the results better and develop predictive models.
As a result, better composite materials are produced with an impact resistance designed to meet practical II.
Materials and Methods This work was done through an experiment and a Multiple InputAeSingle Output (MISO) The study aimed to see how different settings in the making process impact the low-impact performance of the hybrid G.
latifolium/S-glass fibre-reinforced epoxy Three input factors, the ratio of hybridised components, mass fraction, and the way fibres are aligned, were examined for their joint effect on impact response, where the impact energy absorbed was recorded as the output variable.
MISO allowed me to analyse all these influences simultaneously in an orderly way.
Materials and Composite Fabrication The G.
latifolium stem fibres used in this study were locally sourced and processed through a 30-day water submersion retting method, promoting microbial degradation of pectin for effective separation.
After washing and drying, the fibres underwent chemical treatment using a 2% weight/volume sodium hydroxide (NaOH) solution prepared by dissolving 100 g of NaOH in 10,000 ml of distilled water to remove non-cellulosic components and improve interfacial bonding.
The fibres spent 150 minutes in the solution at a temperature of 60AC.
The neutralisation process used 1% acetic acid, and the fibres were washed until their pH level was 7.
The samples were heated to 120AC until their weight became consistent.
Chemical composition analysis of G.
latifolium stem fibres revealed cellulose .
862%), lignin .
301%), hemicellulose .
005%), moisture .
711%), ash content .
095%), crude fibre .
249%), and bulk density .
417 g/m.
Standard techniques .
800AC incineration, 105AC drying, 50 ml pycnomete.
were applied for accuracy, as outlined in Okafor et al.
S-glass fibre, obtained commercially, featured a density of 2.
49 g/cmA, tensile strength of 4,600 MPa, modulus of elasticity of 89 GPa, and elongation of 5.
Bisphenol A.
Epichlorohydrin, and Glycidyl Ester of Neodecanoic Acid are the main Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
ISSN 2580-0817
components of ER-F292 epoxy resin.
The viscosity of the resin was between 1.
5 and 2.
Pa.
ested at 25AC by ASTM D.
, its colour was O 200 Pt-Co .
easured by ASTM D1.
and the density was 1.
13 kg/l .
hecked by ASTM D4.
A 2:1 volume ratio of resin to hardener was adopted to ensure optimal polymerisation and effective fibre-matrix For composite fabrication, moulds were designed with stainless steel, featuring cavities shaped according to ASTM specifications to ensure accurate mechanical testing.
These moulds provided the required geometry for consistent and reproducible test samples from the fibre-resin composite mixtures.
Composite samples were produced using hybridisation ratios of 2.
2 (Level .
8 (Level .
for S-glass to natural fibres, with 21.
24% and 22% fibre mass fractions, respectively.
Fibre orientation was varied from 45A (Level .
to 90A (Level .
Pre-dried at 80AC for 24 hours, fibres were weighed precisely, and the fibre weight fraction .
t%_fibe.
was calculated.
Charpy impact testing assessed the low-velocity impact response of hybrid latifolium/S-glass fibre epoxy composites.
A pendulum hammer struck standard Charpy specimens with centre notches at 3.
8 m/s, and the absorbed impact energy was digitally An optimal experimental design was adopted instead of a full factorial approach to optimise resources, generating 60 strategically selected data points.
These data points represented combinations of three input variables: XCA .
ybridisation rati.
XCC .
ass fraction, %), and XCE .
ibre orientation in degree.
The output variable.
Y, captured the impact response in Joules, providing an understanding of the compositeAos fracture behaviour under sudden loading.
MISO Modelling Approach A Multiple Linear Regression (MLR) model was developed within the MISO framework to understand the relationship between the inputs and the impact response.
The general equation for the regression model is expressed in Eq.
ycU = yu0 yu1 ycU1 yu2 ycU2 yu3 ycU3 yuA a .
In this model.
Y represents the impact response, while XCA.
XCC, and XCE denote the hybridisation ratio, mass fraction, and fibre orientation, respectively.
The term CA serves as the intercept, and CA.
CC, and CE are the regression coefficients that indicate the extent to which each input variable influences the output.
The symbol A captures the residual error or the variation in the response that the model does not explain.
Model Development and Analysis All modelling and statistical analyses were done using SPSS software .
The 60 data points obtained from the optimal design were entered into the software, with categorical variables such as orientation encoded numerically to fit the regression model The regression analysis tested for the statistical significance of each variable using p-values, and confidence intervals were used to assess the reliability of coefficient estimates.
A significance level of 0.
05 was set, meaning that any p-value below this threshold indicated a statistically meaningful effect.
The implementation protocol is captured in Figure 1.
Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
ISSN: 2580-0817 Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
Figure 1: Multiple InputAeSingle Output (MISO) framework Transmission Electron Microscopy Transmission Electron Microscopy (TEM) was conducted by transmitting a beam of electrons through an ultra-thin specimen to examine its internal structure at high resolution.
The sample was placed in the electron microscope.
An electron gun generated a focused beam that passed through electromagnetic lenses and interacted with the specimen.
The transmitted electrons were then magnified and projected onto a fluorescent screen or camera, allowing visualisation of fine structural details at the nanometer scale.
Results and Discussions The datasetAos descriptive statistics .
ith N = .
in Table 1 show that the average hybridisation ratio is 2.
50, and the variability is very low .
tandard deviation = 0.
This suggests a nearly even distribution .
kewness = -0.
with a small, flat peak .
urtosis = 1.
Typically, the mean of the mass fraction is .
79%), with a spread of .
, and the shape is near-normally distributed .
kewness = -0.
kurtosis = -1.
Fibre orientation averages .
90A) with high variability .
, showing minimal skew (-0.
and a relatively flat distribution .
urtosis = -1.
The low velocity impact records a mean of .
82 joule.
, slight positive skew .
, and low peakedness .
urtosis = -0.
Table 1.
Descriptive statistics of hybridisation ratio, mass fraction, fibre orientation, and low velocity impact (N = .
Mean Statistic Statistic Hybridization Ratio (-) Mass Fraction (%) Fiber Orientation (De.
Low Velocity Impact (J) Valid N .
Std.
Deviation Skewness Kurtosis Statistic Statistic Std.
Error Statistic Std.
Error Correlation analysis in Table 2 finds that the variables are associated to different The hybridisation ratio has a weakly negative relationship with low velocity impact .
= -0.
, but this is not statistically significant .
= 0.
Material Compactness has no significant connections to mass fraction .
= -0.
p = 0.
or fibre orientation .
= - Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
ISSN 2580-0817
p = 0.
Low velocity impacts are related to mass fraction through an inverse correlation .
= -0.
, which is statistically significant .
= 0.
, meaning that rises in mass fraction led to lower impact resistance.
Fibre orientation exhibits a very strong negative correlation with low velocity impact .
= -0.
p = 0.
, which is also statistically significant, suggesting that higher orientation angles significantly reduce the impact However, mass fraction and fibre orientation are not meaningfully correlated .
= 0.
p = 0.
, indicating their effects on impact may be independent.
Table 2.
Pearson correlation matrix showing relationships among hybridisation ratio, mass fraction, fibre orientation, and low velocity impact (N = .
Hybridization Ratio (-) Mass fraction
(%)
Fiber (De.
Low velocity Impact (Joule.
Hybridization Ratio (-) Mass fraction (%) Fiber Orientation (De.
Low Velocity Impact (Joule.
Pearson Correlation Sig.
Pearson Sig.
Pearson Sig.
Pearson Sig.
**.
Correlation is significant at the 0.
01 level .
-taile.
Table 3.
Model summary of multiple regression predicting low velocity impact from fibre orientation, mass fraction, and hybridisation ratio Model Square Adjusted R Square Model Summaryb Std.
Change Statistics Error of df1 df2 Estimate Change Change Sig.
Change DurbinWatson Predictors: (Constan.
Fiber Orientation (De.
Mass fraction (%).
Hybridization Ratio (-) Dependent Variable: Low Velocity Impact (Joule.
The regression model in Table 3 reveals a very strong relationship between the predictors and low velocity impact, with a correlation coefficient (R) of .
and a coefficient of determination (RA) of .
, indicating that .
9%) of the variance in low velocity impact is explained by fiber orientation, mass fraction, and hybridisation ratio.
The adjusted RA of 0.
988 confirms the modelAos robustness.
The modest error rate .
proves Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
ISSN: 2580-0817 Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
the model can make accurate predictions.
The modelAos significance is confirmed by F = 390 and p = 0.
no autocorrelation was found in the residuals (Durbin-Watson = Table 4.
ANOVA summary for the regression model predicting low velocity impact Model Sum of Squares Regression Residual Total ANOVAa Mean Square Sig.
Dependent Variable: Low Velocity Impact (Joule.
Predictors: (Constan.
Fiber Orientation (De.
Mass fraction (%).
Hybridization Ratio (-) According to ANOVA in Table 4, the predicted model is significant for low velocity impact, with a regression sum of squares of 9.
624 and a residual sum of squares of 0.
which equals 9.
degrees of freedom are available for regression in the model, with .
more for residuals.
The mean square for regression is .
, while that for residual is .
The F-value is remarkably high at .
, with a significance level of .
= .
, indicating that the predictors jointly have a statistically significant effect on low velocity impact.
The regression coefficients in Table 5 show how each independent variable influences low-velocity impact.
The constant term is .
, indicating the predicted impact when all predictors are zero.
The hybridisation ratio has a negative unstandardised coefficient of .
, showing a significant inverse effect .
= -16.
p = 0.
Mass fraction also negatively affects impact with a coefficient of (-0.
, which is statistically significant .
= p = 0.
Fibre orientation exhibits the strongest influence, with a coefficient of (-0.
and the highest standardised beta value (-0.
, indicating a substantial and significant negative impact .
= -59.
p = 0.
Table 5.
Coefficients of predictors for low velocity impact Coefficientsa Unstandardized Coefficients Standardized Coefficients Std.
Error Beta Model (Constan.
Hybridization Ratio (-) Mass fraction (%) Fiber Orientation (De.
Sig.
Dependent Variable: Low Velocity Impact (Joule.
The P-P plot in Figure 2 assesses the normality of residuals in the regression model predicting low velocity impact.
The points closely follow the diagonal line, indicating that the standardised residuals are approximately normally distributed.
This suggests that the assumption of normality is met, which validates the reliability of the regression results.
Minor deviations from the line are observed at the tails but do not significantly affect the overall pattern.
The observed cumulative probabilities range from 0.
0 to 1.
0, aligning well with the expected values.
This supports the modelAos statistical appropriateness and predictive accuracy.
Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
ISSN 2580-0817
Fig.
Normal p-p plot of regression standardised residual for low velocity impact .
The scatterplot in Figure 3 illustrates the relationship between regression studentised residuals and studentised deleted (PRESS) residuals for the dependent variable, low velocity The data points align almost perfectly along a diagonal line, indicating a strong linear relationship between predicted and observed residuals.
Values on both axes fall approximately within the (Oe2.
range to ( 2.
, with minimal deviation.
This close alignment confirms the modelAos predictive accuracy and the absence of influential outliers.
The pattern suggests that the residuals are stable and consistent, supporting the regression assumptions' validity and the fitted model's reliability.
Fig.
Scatterplot of regression studentized residual vs.
studentized deleted (PRESS) Residual for low velocity impact (Joule.
Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
ISSN: 2580-0817 Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
The TEM images in Figure 4 depict confirmation test sample 1, which combines S-glass fibre with natural fibres oriented at 90 degrees within an epoxy matrix.
Image (A) reveals particle sizes ranging from 10.
02 nm to 22.
83 nm within a 100 nm scale, indicating the dispersion of the fibre-matrix interface.
Image (B) shows a more compact structure within a 50 nm scale, suggesting strong fibre-matrix adhesion.
The projected sizes in Image C span 52 nm to 26.
40 nm, indicating differences in epoxy-reinforcement contact patterns while showing possible dispersion aggregation.
Scales differing between 100 and 50 nm reveal microstructural discrepancies that could affect material properties.
Reinforcement along specific axes is observed through the 90-degree fibre orientation, resulting in anisotropic behaviour.
Fig.
TEM images of the confirmation test sample 1 The descriptive statistics, correlation, and regression analyses of hybridisation ratio, mass fraction, fibre orientation, and low velocity impact offer an understanding of composite materials' structural behaviour under dynamic stress.
Since the hybridisation ratio, mass fraction, fibre orientation, and their low skewness and kurtosis resemble a near-normal distribution, there is no serious indication of non-normal distribution.
However, recent studies discovered that fibre data sets demonstrate skewness, indicating that fibre direction and quality differ among samples .
In a different study, archive items with controlled fabrication methods also demonstrated a similar symmetrical pattern in the variables, which agrees with our observations .
The results in the correlation matrix show that fibre orientation and mass fraction decrease with lower velocity impact in a significant and negative way.
Consistent with the Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
ISSN 2580-0817
findings of Kazemi et al.
, recent research indicates that increasing the fibre orientation angle tends to reduce resistance to low-velocity impact in hybrid composites.
Meanwhile.
Ismail et al.
found that mass fraction had a weak positive effect on impact performance because of better fibre spreading and increased bonding at the materialAos edge.
According to Kureemun et al.
, there seems to be little connection between hybridisation percentage and other factors when the interaction among the fibres is poorly managed.
The model successfully showed how low-velocity impact can be predicted using only the three predictors.
This observation is backed up by a recent study showing that the same independent variables can explain as much as 98% of impact variability .
ahan et al.
noticed a reduced explanation of variations in the model and linked this to differences in the amounts of fibres present.
The Durbin-Watson value confirms the absence of autocorrelation, thereby supporting the modelAos statistical soundness, a finding similarly reported by Shah et al.
in their impact regression study on fibre-reinforced thermosets.
The ANOVA results reinforce the significance of the predictors, with the regression model outperforming the residuals significantly.
This supports prior findings where composite behaviour under stress was reliably predicted using orientation and composition parameters .
, .
, .
The coefficients underscore fibre orientation as the strongest predictor, followed by mass fraction and hybridisation ratio.
This is consistent with the report of Dress et al.
who documented a dominant role of fibre alignment in impact resistance.
In contrast, some studies placed mass fraction ahead in predictive strength, especially in bio-based composites where density and resin bonding played a larger role .
The normal P-P plot and scatterplot both support the validity and reliability of the regression model.
The residualsAo alignment with theoretical expectations indicates that the assumptions of linear regression were satisfied.
In a related study, normal residual distribution indicated model appropriateness in predicting flexural strength using similar variables .
However, deviations were more pronounced in models where additional interaction terms were introduced, leading to complexity and reduced clarity.
Conclusion This study demonstrated the effectiveness of a Multiple InputAeSingle Output (MISO) framework in analysing the low velocity impact response of hybrid Gongronema latifolium/S-glass fibre-reinforced epoxy composites.
The combined effects of three key fabrication parameters, hybridisation ratio, fibre mass fraction, and fibre orientation, were systematically evaluated using a structured experimental design.
Statistical modelling using multiple linear regression revealed that all three variables had significant negative effects on impact performance, with fibre orientation emerging as the most influential factor.
The regression model exhibited a high coefficient of determination (RA = 0.
, indicating that the selected inputs could explain 98.
9% of the variation in impact energy absorption.
Checking the diagrams revealed that the data met all the regression assumptions, such as normality and independent residuals.
Evidence shows that too much or too little of these materials may reduce the compositeAos ability to handle rapid impacts.
Increases in alignment and the amount of fibres caused significant decreases in impact resistance.
It proves why it is critical to optimise the design of composites finely.
It was found that MISO was effective in describing and anticipating the response of materials hit by shocks.
It gives useful instructions for designing bio-hybrid composites that must perform well under impact and be dependable.
Okafor et al.
(MISO framework for Low Velocity Impact Response of Hybrid Fibre Epoxy Composite.
ISSN: 2580-0817 Journal of Mechanical Engineering Science and Technology Vol.
No.
July 2025, pp.
Acknowledgements The publication of this work was supported by the TETFund Centre of Excellence for BioMedical.
Engineering and Agricultural Translational Studies (TCE-BEATS).
Nnamdi Azikiwe University.
Awka.
Anambra State.
References