  Digital image correlation (DIC) is a well-established, non-invasive technique
for tracking and quantifying the deformation of mechanical samples under
strain. While it provides an obvious way to observe incremental and aggregate
displacement information, it seems likely that DIC data sets, which after all
reflect the spatially-resolved response of a microstructure to loads, contain
much richer information than has generally been extracted from them. In this
paper, we demonstrate a machine-learning approach to quantifying the prior
deformation history of a crystalline sample based on its response to a
subsequent DIC test. This prior deformation history is encoded in the
microstructure through the inhomogeneity of the dislocation microstructure, and
in the spatial correlations of the dislocation patterns, which mediate the
system's response to the DIC test load. Our domain consists of deformed
crystalline thin films generated by a discrete dislocation plasticity
simulation. We explore the range of applicability of machine learning (ML) for
typical experimental protocols, and as a function of possible size effects and
stochasticity. Plasticity size effects may directly influence the data,
rendering unsupervised techniques unable to distinguish different plasticity
regimes.
