Neural networks approach to biocomposites processing

Biological neural networks mathematical counterpart artificial neural networks (or neural networks: NN) have contributed to the evolution of a distinct parallel information processing methodology for computational sciences. Problems such as biocomposites modeling or prediction are complicated to model with traditional statistical and mathematical tools due to the inherent noise in data. NN´s efficient parallel processing capability for pattern recognition, forecasting, system analysis, controls and modeling can aid fast prediction, characterization and modeling of novel biocomposites, provided a good knowledge base is available. For the large knowledge base creation, samples with varying flax fiber (0%-35% with 5% interval) load are created with 2 different operating pressures 1 psi and 1.6 psi (variable operating parameters) to produce compression molded biocomposite boards. These boards go through destructive sampling process to contribute to tensile, impact, hardness, flexural and density data. Using this data a number of neural networks using Matlab^® were evaluated to find the optimal neural network architecture. The multilayer feed forward with backpropagation learning (FFBPNN, L₁: 10, L₂:10, L₃: 2) provided best results. It was then further trained with 5 separate training algorithms. Finally the FFBPNN trained with TRAINLM was selected to generate prediction results that were optimal. The trained NN is capable of providing required composition and pressure based on desired mechanical property.