An efficient deep learning model to predict the structural response of CFRP isogrid tubes.

Several sectors have increased the use of lattice structures due its great capacity to reduce the structural mass with reasonable rigidity loss. However, these structures can present complex mechanical behaviors, impossible to be determined analytically. Surrogate models obtained from design of experiments have been shown to be promising but insufficient. Numerical models using finite elements are computationally expensive when optimization model updating is considered. In order to solve these problems, in this paper a deep learning model is trained in order to predict 16 different structural responses (static, dynamic and stability) of a complex composite isogrid structure tube. All input data were generated from a numerical finite element model. The results demonstrated substantial capacity of the deep learning model to fit the isogrid physical behavior and also predict the structural responses. In addition, it was also to predict the design parameters of the isogrid structure from the desired results, also demonstrating the good performance (R2 > 90 %) of the artificial intelligence model to perform this prediction. [ABSTRACT FROM AUTHOR]