Research on cube construction technology based on biaxial testing

Data-driven methods based on machine learning have been successfully used in image processing, data dimensionality reduction and other fields. At present, there is still no theory in the field of composite materials applications that can give highly accurate prediction results under all circumstances. This paper designed biaxial tests of composite materials to obtain multi-dimensional data such as DIC digital speckle data, testing machine load displacement data, and strain data. Based on the fusion algorithm, a multi-dimensional data set that can be used for machine learning was constructed, which will expand the training data-driven model. Data availability. If rich constitutive information is combined with physical models, it can lay the foundation for subsequent data-driven research on failure criteria and mechanisms of composite materials.