A novel integrated BPNN/SNN artificial neural network for predicting the mechanical performance of green fibers for better composite manufacturing

Abstract

Since the mechanical properties of green cellulosic fibers are only determined experimentally with high diversity, introducing prediction methods for such intrinsic properties of fibers based upon chemical composition is of paramount importance. In this work, double integrated artificial neural networks are utilized to perform novel predictions and classifications of the intrinsic mechanical properties of natural fibers to enhance their implementations in green bio-composites. The predictions and classifications were based upon the green fibers’ chemical composition. The developed model was built up utilizing experimental data for mechanical properties of various cellulosic fiber types commonly used in natural fiber reinforced composites. The first stage back propagation neural network (BPNN) was developed to predict the intrinsic mechanical properties, while the second stage was used to classify the natural fibers based upon shallow neural networks (SNN). The second layer of the model was built based upon shallow neural networks that was capable of classifying the natural fibers. The SNN model was built with 20 hidden layer size and the number of output neurons was set to 11, which equals to the number of fibers classes specified by the dataset. The developed model was capable of predicting all of Young’s modulus, ultimate tensile strength, and elongation at break properties from only two intrinsic properties of fibers; cellulose and moisture content. The overall percentage for the correct classifications was 95.6%, which had demonstrated that machine learning offers a robust approach to classify the fibers based on their intrinsic mechanical properties. The presented model would dramatically help designers and engineers to enhance their selections of various cellulosic fibers for better reliable green composite material performance.