基于机器学习的梯度点阵材料优化设计

Lattice materials possess the characteristics of light weight, impact resistance, high energy absorption, so that they can be applied broadly in bearing part design of aero craft. The dynamic mechanical properties of the lattice materials under high speed impact can be improved by reasonably design of the internal bar diameter of the lattice materials. In this paper, employing simulation data, the dynamic mechanical response prediction and structural parameter optimization of graded lattice materials were carried out based on random forest model. Firstly, taking FCC graded lattice structure as study object, a gradient design of lattice material density was realized by adjusting the bar diameter parameters. And then, keeping the relative density of the whole lattice unchanged, the dynamic mechanical response of the graded lattice materials with different density distribution under impact loading was calculated based on LS-DYNA software, including the contact stress curve of the impact face and the support face over time. Finally, based on random forest model, taking the relative density of cells in each layer as input, the peak stress on the end face of lattice materials was predicted, and the cell layer with the greatest influence on the peak stress at different end face positions was analyzed with Gini index. And, connecting the grid search algorithm with a well trained random forest model, and taking the peak stress at the two end faces as the optimization objectives, the optimal value of cell rod diameter of the lattice material was obtained. The prediction error of the model is less than 5%. The numerical simulation results show that the corresponding peak stress of the optimized gradient lattice material is higher than that of any structure in the simulation data set.