  A variant of the U-Net convolutional neural network architecture is proposed
to estimate linear elastic compatibility stresses in a-Zr (hcp) polycrystalline
grain structures. Training data was generated using VGrain software with a
regularity alpha of 0.73 and uniform random orientation for the grain
structures and ABAQUS to evaluate the stress welds using the finite element
method. The initial dataset contains 200 samples with 20 held from training for
validation. The network gives speedups of around 200x to 6000x using a CPU or
GPU, with signifcant memory savings, compared to finite element analysis with a
modest reduction in accuracy of up to 10%. Network performance is not
correlated with grain structure regularity or texture, showing generalisation
of the network beyond the training set to arbitrary Zr crystal structures.
Performance when trained with 200 and 400 samples was measured, finding an
improvement in accuracy of approximately 10% when the size of the dataset was
doubled.
