Micromechanics-based Artificial Neural Networks and Transfer Learning for Modeling Short Fibre Reinforced Composites in Automotive Applications

Abstract

For the automotive industry, improving efficiency is crucial while weight reduction is one key factor for high efficiency. Short Fiber Reinforced Composites (SFRCs) provide superior performance at reduced weight and are suitable for mass production, making them attractive materials for the industry. However, the mechanical modeling of SFRCs poses challenges. A full field analysis may take multiple trials to generate a proper realization, and subsequent analysis can take hours or days to finish. Furthermore, the mechanical response is influenced by fiber orientation and volume fraction, which can have countless configurations. Therefore, data-driven models for SFRCs have gained popularity. Previous work utilized mean-field analysis results to train a recurrent neural network, aiming to predict elasto-plastic stress response of SFRCs with different strain paths and properties. This study enhanced the mean-field network with a limited amount of full-field data, aiming to improve the network’s prediction accuracy to a full-field level.