Spatial strain correlations, machine learning, and deformation history in crystal plasticity
Andrew C. Reid, Stefanos Papanikolaou, Hengxu Song, Erik Van der Giessen, Stephen A. Langer, Michail Tzimas
Digital image correlation (DIC) is a well-established, non-invasive technique for tracking and quantifying the deformation of mechanical samples under test. 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, likely 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 systems response to the DIC test load. Our playground consists of deformed crystalline thin films generated by a discrete dislocation plasticity simulation. We explore the range of applicability of 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 ML techniques unable to distinguish different plasticity regimes.
, Papanikolaou, S.
, Song, H.
, Van, E.
, Langer, S.
and Tzimas, M.
Spatial strain correlations, machine learning, and deformation history in crystal plasticity, Journal of the Mechanics and Physics of Solids, [online], https://doi.org/10.1103/PhysRevE.99.053003
(Accessed July 4, 2022)