Image-based inverse characterization of in-situ microscopic composite properties

An inverse characterization approach to identify the in-situ elastic properties of composite constituent materials is developed. The approach relies on displacement measurements available from image-based measurement techniques such as digital image correlation and template matching. An optimization problem is formulated, where the parameters of an assumed functional form describing spatially variable material properties are obtained by minimizing the discrepancies between noisy displacement measurements and the corresponding simulated values. The proposed formulation is analyzed from a statistical inference theory standpoint. It is shown that the approach exhibits estimation consistency, i.e. given noisy input data the identified material properties converge to the true material properties as the number of available measurements increases. The performance of the proposed approach is evaluated by a series of virtual characterizations that mimic physical characterization tests in which fiber centroid displacements are obtained through fiber template matching. The virtual characterizations demonstrate that the effect of measurement noise in identifying the in-situ constituent properties can be mitigated by selecting a sufficiently large measurement dataset. The numerical studies also show that, given a rich measurement dataset, the proposed approach is able to describe increasingly complex spatial variation of properties.