Application of Artificial Neural Network for Parameters Optimization on Tensile Properties via Hand Lay-up Techniques for Kenaf/Fibreglass Reinforced Hybrid Composite

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The growing demand for sustainable, high-performance materials has led to the development of hybrid composites that integrate natural and synthetic fibers.This study explores the tensile properties of kenaf/fiberglass reinforced polyester hybrid composites fabricated through the hand lay-up technique.Composites incorporating varying kenaf fiber weight percentages (15%, 45%, 60%, and 75%) were evaluated in accordance with American Society for Testing and Materials (ASTM) D3039 standards.Additionally, an Artificial Neural Network (ANN) model was constructed to predict tensile strength based on fiber content, composite thickness, and defect levels.The model was trained using three different algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG).The composite with 45% kenaf fiber demonstrated the optimal tensile performance.The highest measured tensile strength reached 50.47 MPa.The ANN model achieved a high prediction accuracy with a correlation coefficient (R) of 0.9686 and a Mean Squared Error (MSE) of 0.0063.Among the training algorithms, the LM algorithm outperformed the others in terms of prediction accuracy.These findings highlight the effectiveness of ANN modelling in optimizing hybrid composite formulations, minimizing experimental requirements, and advancing their use in structural applications.

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