An effective hybrid particle swarm—artificial neural network optimization for predicting green bio-fiber mechanical characteristics and optimizing biomaterial performance

Abstract

Abstract Natural fiber-reinforced composites are currently utilized in several applications due to worldwide environmental and cost concerns. However, these composites have production challenges such as poor reinforcement-matrix adhesion, that sophisticates the prediction of their mechanical properties. This study presents a novel, robust hybrid particle swarm—artificial neural network optimization (PSO-ANN) methodology to assess and create accurate predictions of the green bio-fibers to optimize and improve the mechanical features of biomaterials for green bio-products instead of performing tedious experimental works. As the mechanical qualities of green bio-fibers might differ from one fiber to another due to several interacted parameters, high complexity in predicting the bio-fiber capabilities exists. Therefore, this work utilizes suitable methods with a non-linear activation function to predict the mechanical characteristics of natural fibers that allow the researchers to improve the choices of natural fibers for biomaterials on the basis of cellulose content, the microfibrillar angle, and the diameter of natural fibers, decreasing the duration of the process required to characterize materials experimentally. The reliability of the introduced PSO-ANN model was verified by the investigations of the fiber’s tensile stress and Young’s modulus. Results showed that the presented model is capable of consistently and accurately monitoring the mechanical performance to a large degree, in comparison with experimental results. This in fact would facilitate and simplify the process of selecting the best natural fiber composites, which speeds up the experimental characterization phase and improves energy efficiency in the process of converting energy into monetary income, which would have ramifications for both economies and ecosystems. The anticipated method would also boost scientific evaluation of green fibers, confirming their role as a replacement material for green product fulfillment in future eco-friendly manufacturing.