Multiobjective optimization for the crashworthiness design of bioinspired sinusoidal honeycombs

The bioinspired sinusoidal honeycomb (BSH) structure has been proved to have excellent energy absorption performances. To further increase its performance and optimize its structural parameters, the surrogate model of specific energy absorption (SEA) and initial peak crushing force (PCF) is constructed. Non-dominated sorting genetic algorithm II is used to carry out the multi-objective optimization for BSH. Parametric studies show that the relationship between the SEA and amplitude is not obvious, while the PCF increases with the increase of amplitude, and both the SEA and PCF increase as the wall thickness increases. Moreover, the Pareto frontiers of the four BSHs with different wavenumbers have a certain similarity. Compared with the original solution, the optimal solution of the BSH is 2.5 T under the constraints, where the PCF decreases by 15.52%, and the SEA increases by 6.77%. Finally, the accuracy of the optimal solution is verified numerically.