Learning crystal plasticity using digital image correlation: Examples from discrete dislocation dynamics
This work addresses the challenge of better utilizing DIC data for materials science, though it is incremental as it builds on existing techniques in a specific domain.
The authors tackled the problem of extracting richer information from digital image correlation (DIC) data by developing a machine-learning approach to quantify the prior deformation history of crystalline samples, demonstrating its applicability in simulated thin films with considerations for size effects and stochasticity.
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.