Multi-objective bulk scale optimisation of an auxetic structure to enhance protection performance

This paper aims to optimise a bulk scale design of a novel auxetic structure, the hourglass structure (HGS), through a multi-objective optimisation model to improve its protection performance. A 3D numerical model of the HGS under an in-plane quasi-static compression was developed and validated with experimental results. Based on the validated numerical model, a series of numerical analyses were conducted by automating the HGS design process with randomly generated design variables. The automated numerical analyses built a dataset of the selected protective performance indicators (namely, peak elastic stress, plateau stress, and energy absorption capacity). Then, the dataset was used to develop a high-accuracy surrogate model using a radial basis function (RBF) neural network. Afterward, the Pareto optimal solutions were searched with the non-dominated sorting genetic algorithm (NSGA-II). The best compromise design out of the Pareto optimal set was determined with the ideal point method. The performance of the optimum design was simulated under both quasi-static and blast loadings to comprehensively explore the protective performance. In addition, a correlation matrix was constructed to investigate the effects of each design parameter on the protective performance indicators quantitatively. The results showed that the obtained optimum design outperformed the baseline structure under both quasi-static and blast loadings. The optimum HGS design displayed a higher and more stable negative Poisson's ratio along with two deformation modes leading to two plateau stress levels. The optimised HGS design is applicable as the core of high-performance protective sandwich structures.

Automated summary

This paper optimises the design of an auxetic structure called the hourglass structure (HGS) to improve its protection performance. A 3D numerical model is developed and validated through experiments, and then automated numerical analyses are conducted with randomly generated design variables to determine the best compromise design. The optimized HGS design outperforms the baseline structure under both quasi-static and blast loadings, making it suitable for high-performance protective sandwich structures.