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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Bioresources 2024-04, Vol.19 (2), p.3461-3474
Hauptverfasser: Mohd Nor Azmi Ab Patar, Nur Aini Sabrin Manssor, Mohd Rashdan Isa, Nur Auni Izzati Jusoh, Mohd Juzaila Abd Latif, Praveena N. Sivasankaran, Jamaluddin Mahmud
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:1930-2126