A database construction method for data-driven computational mechanics of

A new method combining computational homogenization and the Artificial Neural Network (ANN) is proposed to construct elastoplastic composites database efficiently for data-driven computational mechanics (DDCM). The numerical calculations are performed on the representative volume element (RVE) of elastoplastic composites to collect a small set of high-fidelity data containing stress and strain tensors, which is then enriched using ANN to provide sufficient data for DDCM. To justify the validity and efficiency of the proposed method, two composite plates made of matrix-round inclusion material and fiber reinforced material, respectively, are considered. The former is composed of nonlinear materials, while the latter has nonlinearity caused by fiber buckling and plasticity of matrix in the RVE. ANN shows good ability to learn nonlinear relationships between stress and strain from the data collected by computational homogenization, and to generate new data efficiently. This work is expected to uncover the possibility of applying the data generated by artificial intelligence to DDCM in solving mechanical problems of composites. Comparisons demonstrate that the proposed method not only reduces the computational cost for database construction, but also shows excellent accuracy of DDCM even for three-dimensional problems.