Deep DIC

A robust deep leanring enabled digital image correlation with synthetic dataset generator.

Digital image correlation (DIC) has become an industry standard to retrieve accurate displacement and strain measurement in tensile testing and other material characterization. Though traditional DIC offers a high precision estimation of deformation for general tensile testing cases, the prediction becomes unstable at large deformation or when the speckle patterns start to tear. In addition, traditional DIC requires a long computation time and often produces a low spatial resolution output affected by filtering and speckle pattern quality.

To address these challenges, we propose a new deep learning-based DIC approach – Deep DIC (Yang et al., 2022), in which two convolutional neural networks, DisplacementNet and StrainNet, are designed to work together for end-to-end prediction of displacements and strains. Deep DIC gives highly consistent and comparable predictions of displacement and strain with those obtained from commercial DIC software, while it outperforms commercial software with very robust strain prediction even at large and localized deformation and varied pattern qualities. In addition, Deep DIC is capable of real-time prediction of deformation with a calculation time down to milliseconds.

Codes are released on GitHub.



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    Ru Yang ,  Yang Li ,  Danielle Zeng ,  and  Ping Guo
    Journal of Materials Processing Technology, 2022