Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

Abstract

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships. Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties. However, the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches. In order to avoid the complex, cumbersome, and labor-intensive experimental and numerical modeling approaches, a machine learning (ML) model is proposed here such that it takes the microstructural image as input with a different range of Young's modulus of carbon fibers and neat epoxy, and obtains output as visualization of the stress component S₁₁ (principal stress in the x-direction). For obtaining the training data of the ML model, a short carbon fiber-filled specimen under quasi-static tension is modeled based on 2D Representative Area Element (RAE) using finite element analysis. The composite is inclusive of short carbon fibers with an aspect ratio of 7.5 that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition (SSI) process. The study reveals that the pix2pix deep learning Convolutional Neural Network (CNN) model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young's modulus with high accuracy. The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum, indicating excellent prediction capability. In this paper, we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens. The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

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