数据驱动梯度结构材料弹塑性本构

梯度结构材料因其优异的力学性能被广泛应用于工程结构中。本文整合塑性理论和人工神经网络技术，发展了一种构建梯度结构材料弹塑性本构模型的新方法。该方法基于梯度结构材料不同位置的微结构，构建不同代表性体积单元，进而生成应力应变数据，应用生成的数据训练人工神经网络，建立基于神经网络的材料本构模型。应用该方法，本文开展了针对实际工程结构件的计算，算例结果表明，该方法可快速计算梯度功能复合材料在循环载荷反向载荷状态下的宏观响应，且较为准确。该方法为模拟含复杂梯度结构材料的结构件弹塑性力学响应提供了新的工具。

Data-driven elastoplastic constitutive law of gradient structure materials

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.