Quantifying and Predicting the Tensile Properties of Silicone Reinforced with Moringa oleifera Bark Fibers

To obtain a better understanding of using Moringa oleifera bark (MOB) as a reinforcement in a silicone matrix, this study aimed to define the mechanical properties of this new material under uniaxial tension. Composite samples of 0 wt%, 4 wt%, 8 wt%, 12 wt%, and 16 wt% MOB powder were produced. The...

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Bibliographic Details
Published in:BioResources
Main Author: Ab Patar M.N.A.; Manssor N.A.S.; Isa M.R.; Jusoh N.A.I.; Abd Latif M.J.; Sivasankaran P.N.; Mahmud J.
Format: Review
Language:English
Published: North Carolina State University 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191732886&doi=10.15376%2fbiores.19.2.3461-3474&partnerID=40&md5=5507bb206ab72ba28eaea74064a06710
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Summary:To obtain a better understanding of using Moringa oleifera bark (MOB) as a reinforcement in a silicone matrix, this study aimed to define the mechanical properties of this new material under uniaxial tension. Composite samples of 0 wt%, 4 wt%, 8 wt%, 12 wt%, and 16 wt% MOB powder were produced. The tensile properties were quantified mathematically using the neo-Hookean hyperelastic model. The collected data were employed to establish multiple inputs of an artificial neural network (ANN) to predict its material constant via MATLAB. The result showed that the material constant for the 16 wt% fiber content sample was 63.9% higher than pure silicone. This was supported by the tensile modulus testing, which indicated that the modulus increased as the fiber content increased. However, the elongation ratio (λ) of the MOB-silicone biocomposite decreased slightly compared to the pure silicone. Lastly, the prediction of the material constant using an ANN recorded a 2.03% percentage error, which showed that it was comparable to the mathematical modelling. Therefore, the inclusion of MOB fibers into silicone produced a stiffer material and gradually improved the composite. Furthermore, the network that had multiple inputs (weighting, load, and elongation) was more reliable to produce precise predictions. © 2024, North Carolina State University. All rights reserved.
ISSN:19302126
DOI:10.15376/biores.19.2.3461-3474