  Evaluating the mechanical response of fiber-reinforced composites can be
extremely time consuming and expensive. Machine learning (ML) techniques offer
a means for faster predictions via models trained on existing input-output
pairs and have exhibited success in composite research. This paper explores a
fully convolutional neural network modified from StressNet, which was
originally for lin-ear elastic materials and extended here for a non-linear
finite element (FE) simulation to predict the stress field in 2D slices of
segmented tomography images of a fiber-reinforced polymer specimen. The network
was trained and evaluated on data generated from the FE simulations of the
exact microstructure. The testing results show that the trained network
accurately captures the characteristics of the stress distribution, especially
on fibers, solely from the segmented microstructure images. The trained model
can make predictions within seconds in a single forward pass on an ordinary
laptop, given the input microstructure, compared to 92.5 hours to run the full
FE simulation on a high-performance computing cluster. These results show
promise in using ML techniques to conduct fast structural analysis for
fiber-reinforced composites and suggest a corollary that the trained model can
be used to identify the location of potential damage sites in fiber-reinforced
polymers.
