Modeling and Investigation of the Influential Reinforcement Parameters on the Strength of Polypropylene Lignocellulosic Fiber Composites Using Analysis of Variances and Box-Cox Transformation Technique

Al-Shrida, Mu’ayyad M.; Hayajneh, Mohammed T.; AL-Oqla, Faris M.

doi:10.1590/1980-5373-mr-2022-0386

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Yusof et al (2016) applied the Taguchi method to find the best fiber-matrix combinations and their weight ratio for optimal tensile strength (Yusof et al, 2016). Some other researchers used different methods for optimizing the natural fiber composites (Al-Shrida et al, 2023;Chaudhuri et al, 2013;Fares et al, 2019;Yaghoobi & Fereidoon, 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers

Mohammad Rababah,

Mohammed AL-Oqla

2023

JST

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Natural fiber composites are potential alternatives for synthetic materials due to environmental issues. The overall performance of the fiber composites depends on the reinforcement conditions. Thus, this work aimed to optimize the reinforcement conditions of the natural fiber composites to improve their mechanical performance via applying an integrated scheme of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and differential evolution (DE) methods considering various reinforcement conditions including fiber length, fiber loading, and treatment time for optimal characteristics of the composite mechanical performance. The B-Spline approximation function was adopted to predict the experimental performance of green composites. The B-Spline approximation function demonstrated incomparable accuracy compared to linear or quadratic regressions. The function is then optimized using an integrated optimization method. Results have demonstrated that optimal reinforcement conditions for the maximized desired mechanical performance of the composite were achieved with high accuracy. The robustness of the proposed approach was approved using various surface plots of the considered input-output parameter relations. Pareto front or the non-dominated solutions of the desired output mechanical properties were also obtained to demonstrate the interaction between the desired properties to facilitate finding the optimal reinforcement conditions of the composite materials.

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Section: Introductionmentioning

confidence: 99%

Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers

Mohammad Rababah,

Mohammed AL-Oqla

2023

JST

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“…Results of several experimental studies on natural fibers' mechanical properties[7,[20][21][22][23][24][25][26].…”

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confidence: 99%

An effective hybrid particle swarm—artificial neural network optimization for predicting green bio-fiber mechanical characteristics and optimizing biomaterial performance

Nawafleh,

Al-Oqla

2024

Funct. Compos. Struct.

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Natural fiber-reinforced composites are currently utilized in several applications due to worldwide environmental and cost concerns. However, these composites have production challenges such as poor reinforcement-matrix adhesion, that sophisticates the prediction of their mechanical properties. This study presents a novel, robust hybrid particle swarm – artificial neural network optimization (PSO-ANN) methodology to assess and create accurate predictions of the green bio-fibers to optimize and improve the mechanical features of biomaterials for green bio-products instead of performing tedious experimental works. As the mechanical qualities of green bio-fibers might differ from one fiber to another due to several interacted parameters, high complexity in predicting the bio-fiber capabilities exists. Therefore, this work utilizes suitable methods with a non-linear activation function to predict the mechanical characteristics of natural fibers that allow the researchers to improve the choices of natural fibers for biomaterials on the basis of cellulose content, the microfibrillar angle, and the diameter of natural fibers, decreasing the duration of the process required to characterize materials experimentally. The reliability of the introduced PSO-ANN model was verified by the investigations of the fiber’s tensile stress and Young’s modulus. Results showed that the presented model is capable of consistently and accurately monitoring the mechanical performance to a large degree, in comparison with experimental results. This in fact would facilitate and simplify the process of selecting the best natural fiber composites, which speeds up the experimental characterization phase and improves energy efficiency in the process of converting energy into monetary income, which would have ramifications for both economies and ecosystems. The anticipated method would also boost scientific evaluation of green fibers, confirming their role as a replacement material for green product fulfillment in future eco-friendly manufacturing.

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Thermal stability analysis and mechanical performance of Mediterranean lignocellulosic fiber reinforced polypropylene sustainable composites

AL-Oqla,

Hayajneh,

Al-Shrida

2024

Journal of Industrial and Engineering Chemistry

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Modeling and Investigation of the Influential Reinforcement Parameters on the Strength of Polypropylene Lignocellulosic Fiber Composites Using Analysis of Variances and Box-Cox Transformation Technique

Cited by 4 publications

References 35 publications

Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers

Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers

An effective hybrid particle swarm—artificial neural network optimization for predicting green bio-fiber mechanical characteristics and optimizing biomaterial performance

Thermal stability analysis and mechanical performance of Mediterranean lignocellulosic fiber reinforced polypropylene sustainable composites

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