Computation of Full-field Strains Using Principal Component Analysis

The primary output from several full-field deformation measurement techniques, e.g., Digital Image Correlation (DIC), is the displacement vector at a dense grid of points covering the area of interest. Since such displacement data inherently contain noise, they are usually smoothed first and then differentiated to obtain strains. Another common approach is to use finite-element shape functions for the strains and compute them by treating the measured displacements as nodal displacements. In this paper, we propose a novel method for strain calculation from full-field data, based on the multivariate analysis technique of Principal Component Analysis (PCA) using which we first obtain the singular values and singular vectors for each component of the displacement field. By choosing only the dominant singular values and their corresponding singular vectors, we show that the dimensionality of the displacement data is sharply reduced and a significant portion of the noise is eliminated. Moreover, the shapes of the dominant singular vectors offer physical insight into dominant deformation patterns. We demonstrate the accuracy of the proposed technique by applying it to two cases each of homogeneous and inhomogeneous strain fields and show that in all cases the proposed method yields excellent results.