Physics-Informed Neural Network Based Digital Image Correlation Method

Digital Image Correlation (DIC) is a key technique in experimental mechanics for full-field deformation measurement, traditionally relying on subset matching to determine displacement fields. However, selecting optimal parameters like shape functions and subset size can be challenging in non-uniform deformation scenarios. Recent deep learning-based DIC approaches, both supervised and unsupervised, use neural networks to map speckle images to deformation fields, offering precise measurements without manual tuning. However, these methods require complex network architectures to extract speckle image features, which does not guarantee solution accuracy This paper introduces PINN-DIC, a novel DIC method based on Physics-Informed Neural Networks (PINNs). Unlike traditional approaches, PINN-DIC uses a simple fully connected neural network that takes the coordinate domain as input and outputs the displacement field. By integrating the DIC governing equation into the loss function, PINN-DIC directly extracts the displacement field from reference and deformed speckle images through iterative optimization. Evaluations on simulated and real experiments demonstrate that PINN-DIC maintains the accuracy of deep learning-based DIC in non-uniform fields while offering three distinct advantages: 1) enhanced precision with a simpler network by directly fitting the displacement field from coordinates, 2) effective handling of irregular boundary displacement fields with minimal parameter adjustments, and 3) easy integration with other neural network-based mechanical analysis methods for comprehensive DIC result analysis.

A Short Image Series Based Scheme for Time Series Digital Image Correlation

A new scheme for digital image correlation, i.e., short time series DIC (STS-DIC) is proposed. Instead of processing the original deformed speckle images individually, STS-DIC combines several adjacent deformed speckle images from a short time series and then processes the averaged image, for which deformation continuity over time is introduced. The deformation of several adjacent images is assumed to be linear in time and a new spatial-temporal displacement representation method with eight unknowns is presented based on the subset-based representation method. Then, the model of STS-DIC is created and a solving scheme is developed based on the Newton-Raphson iteration. The proposed method is verified for numerical and experimental cases. The results show that the proposed STS-DIC greatly improves the accuracy of traditional DIC, both under simple and complicated deformation conditions, while retaining acceptable actual computational cost.