Strain measurement during tensile testing using deep learning-based digital image correlation

This paper describes a novel non-contact strain measurement method defined as deep learning based on digital image correlation (DDIC). In particular, it is very difficult to measure directly displacement of gauge length during tensile testing of thin films. Therefore, we obtained the image data continuously to observe the behavior of the material during tensile testing. The sequential image data obtained at a specific position is assigned to a multi-channel input to train the deep neural network. As a result, the multi-channel image is composed of sequential images obtained along the time domain. Since these images have a correlation with each other along the time domain in each pixel, the neural network learns displacement, including temporal information. The DDIC method originates from a 3D convolutional neural network, which can extract both the spatial and the temporal domain features at the same time. A 3D convolutional filter is used as the feature extraction part of the network in order to effectively learn the input data. The deep learning is an end-to-end learning method and the deformation between two images can be measured regardless of the parameters for the nonlinear deformation of the images. An elastic modulus of 118 GPa, 0.2% yield strength of 941 MPa, ultimate tensile strength of 1108 MPa and fracture strain of 0.02414 are estimated by applying the DDIC method during a tensile test of BeCu thin film. The results of the DDIC method are compared with the displacement sensor data and digital image correlation data.