Prediction method of impact deformation mode based on multimodal fusion with point cloud sequences: Applied to thin-walled structures

Abstract

One of the challenges in solving the structural crashworthiness optimisation problem under impact deformation constraints is to construct an efficient and concise representation method for structural impact deformation. This representation method is expected to include as much information about the structural impact deformation as possible and be computable through numerical simulation, so that it can be combined with the structural impact process. Based on the comparison among several methods, a representation form is proposed using point cloud sequences of finite element nodes in the structural impact process, and the relevant construction method for the point cloud dataset is provided. The dataset includes point clouds derived from the impact deformation and the initial finite element node, structural design variables, and crashworthiness response values of the thin-walled tube during impact process. Based on the constructed dataset, a multi-modal fusion prediction method for the impact process of thin-walled structures is proposed to ensure the efficiency and reliability of crashworthiness prediction with unstructured data. The surrogate model takes structural design variables and initial deformation representations as inputs while impact deformation representations and crashworthiness responses as outputs. The relative errors of the surrogate model's predictions for the crashworthiness responses and impact deformation point cloud are within 5 % of the numerical results. Moreover, the computational speed is 8169 times faster than finite element simulation, ensuring solution accuracy while significantly improving the efficiency of structural crashworthiness prediction. Additionally, the robustness of the surrogate model and the importance of each module are verified and tested through experimental design, providing reference for further improvements to the surrogate model.