Abstract：Gradient structure materials are widely used in engineering applications due to their excellent mechanical properties. However, because its composition or microstructure varies gradually with spatial position and the related mechanical properties, simulating engineering structures containing such materials often faces great challenge and further limits the applications. In order to simulate this kind of materials, the present work proposes a new approach to construct elastoplastic constitutive law of gradient structure materials by integrating the plastic theory and machine learning technology. The proposed approach first builds different representative volume elements (RVE) based on the microstructures at different positions to generate homogenized stress-strain data numerically and then train an artificial neural network (ANN) through the generated data. Then the trained ANN can be used as the homogenized elastic-plastic material model for the gradient structure materials, replacing the yield function in conventional plasticity. The obtained homogenized material model is implemented through UMAT interface of ABAQUS for ease of use. Illustrative example shows that the constitutive model constructed by the present approach can be used to solve the boundary value problems involving elastoplastic materials with gradient structure under complex loading paths (e.g., cyclic/reverse loading) effectively. Compared with the results of direct numerical simulation on the gradient microstructure, the accuracy of the proposed approach in computing the macroscopic mechanical response is verified. But the degree of freedom of the engineering structure and the computational cost are greatly reduced. This approach provides a new way for computing the elastoplastic macroscopic mechanical response of structure involving complex gradient structure materials. At present, the approach can only be used for materials with isotropic hardening and meeting the associated flow law. Further research is needed to overcome these issues.
2021, Vol. 42 
