This study explores the relationship between natural fiber filling density (10%, 15%, 25%) and its impact on the bending properties of polymer compounds reinforced with Diss, Sisal and Luffa fibers. Using advanced techniques like fiber analysis and Fourier transform infrared spectrometry (FTIR), the research reveals that a 25% filling density results in the highest stress values (25.61 MPa, 22.21 MPa and 20.88 MPa) for Diss, Sisal and Luffa compounds, respectively, fostering robust bonds in Diss-reinforced polymers. The Artificial Neural Network (ANN) model demonstrates superior predictive capability with correlation coefficients exceeding 0.99 for stress and displacement, outperforming Response Surface Methodology (RSM). Analysis of Variance (ANOVA) underscores the impact of sample section parameters and fiber rate on stress, establishing the significance of type parameters and fiber rate on displacement. This integration of ANN and RSM represents a paradigm shift in predicting bending mechanical properties, advancing our understanding of composite materials for innovative applications.
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