Design and Optimization of Corrugated Multi-cell Gradient Structures Based on Machine Learning

To address the collision protection requirements in fields such as aerospace, transportation, and civil engineering, a novel design method for the corrugated multi-cell gradient hexagonal tube (CMGHT) is proposed: sinusoidal corrugated ribs are introduced into a conventional hexagonal tube, integrated with the functional gradient design concept to enhance the structural crashworthiness. First, the FE model of the structure was established and numerical simulation analysis was conducted. Results indicate that under the same wall thickness condition, the key energy absorption indicators of CMGHT outperform existing structures significantly. Compared with the hexagonal tube (HT), the energy absorption (EA), specific energy absorption (SEA), mean crushing force (MCF), and crushing force efficiency (CFE) are improved by 395%, 76%, 45%, and 395%, respectively; Compared with the multi-cell hexagonal tube (MHT), the aforementioned indicators are increased by 102%, 57%, 120%, and 48%, respectively; Relative to a corrugated multi-cell hexagonal tube (CMHT), the enhancements are 8%, 7%, 8%, and 32% respectively, while the initial peak crushing force (IPCF) is decreased by 18%. These results fully demonstrate its superior energy absorption performance. Subsequently, the geometric parameters of the ribs and outer tube were selected as design variables. A total of 540 sample sets were generated via full factorial experimental design, and a support vector machine (SVM) surrogate model was constructed. Combined with the crested porcupine optimization (CPO) algorithm, model optimization was completed to achieve accurate prediction of CMGHT’s crashworthiness. Finally, the multi-objective coati optimization algorithm (MOCOA) was adopted for multi-objective optimization to obtain the optimal combination of characteristic parameters. Optimization results show that compared with the initial structure, the SEA of the optimized structure is increased by 22%, the CFE by 53%, and the MCF by 270%, further verifying the effectiveness of the proposed design method.