Experimental-numerical analysis of leucaena-silicone biocomposite on physical-mechanical properties

Natural fibre composites have become popular for their ability to support sustainable and economic growth by using renewable resources and lowering environmental impact. These composites, made by combining different materials, are designed to have specific desirable properties. Therefore, this study focuses on creating an innovative silicone biocomposite by reinforcing Leucaena fibre into silicone rubber. The research defines in two main stages which are experiments and mathematical modelling. Despite the lack of reliable data on reinforcing Leucaena into silicone rubber, specimens were carefully prepared with fibre compositions ranging from 0wt% to 16wt%, following appropriate fabrication standards. Comprehensive physical and mechanical tests were conducted to evaluate the properties of the new material, including its density and tensile behaviour under uniaxial load. The tests performed included the Density Test (ASTM-D792), Uniaxial Tensile Test (ASTM-D412) and Compression Set Test (ASTM D394). These tests were conducted to provide a thorough understanding of the material’s performance under various conditions. As the materials exhibit hyperelastic behavior, three hyperelastic material models (Neo-Hookean, Mooney-Rivlin, and Ogden) were employed to quantify the material parameters. This study utilized a predictive approach employing the MATLAB Neural Network Tool (nntool) to construct an artificial neural network (ANN) with three inputs (weight, load, and elongation) and outputs (material constants: C1, C2, μ, and α). The network successfully predicted the material constants of the Ogden model. Testing results showed a consistent increase in specimen density with the rise in fibre content. The quantified tensile properties of this newly introduced Leucaena-silicone biocomposite ranges from 30.30 kPa to 38.90 kPa (Neo-Hookean), 42.30 kPa to 47.10 kPa (Mooney Rivlin) and 5.40 kPa to 55.90 kPa (Ogden). Thus, tensile test results demonstrated improved material properties and increase specimen stiffness with higher fibre content supported by numerical analysis. The Coefficient of determination, R², aided in selecting the best curve fitting in hyperelastic modelling with the Ogden model exhibiting the highest R² value (0.99903), indicating its excellent agreement with experimental data. Besides, the percentage difference of prediction between experimental and ANN is 0.11% (Neo Hookean). The artificial neural network efficiently predicted the optimum trained network through various trials, achieving satisfactory coefficients of correlation (R) for training, testing, validation and overall. In conclusion, this study significantly advances the understanding of Leucaena-silicone biocomposites by combining experimental and numerical analyses to evaluate and predict material properties. The findings contribute to the development of sustainable materials with improved mechanical performance. The predictive model established in this research will aid future studies in efficiently determining material constants, promoting further innovation in biocomposite materials.