In the pursuit of designing safer and more efficient energy-absorbing structures, engineers must tackle the challenge of improving crush performance while balancing multiple conflicting objectives, such as maximising energy absorption and minimising peak impact forces. Accurately simulating real-world conditions necessitates the use of complex material models to replicate the non-linear behaviour of materials under impact, which comes at a significant computational cost. This study addresses these challenges by introducing a multi-objective Bayesian optimisation framework specifically developed to optimise spinodoid structures for crush energy absorption. Spinodoid structures, characterised by their scalable, non-periodic topologies and efficient stress distribution, offer a promising direction for advanced structural design. However, optimising design parameters to enhance crush performance is far from straightforward, particularly under realistic conditions. Conventional optimisation methods, although effective, often require a large number of costly simulations to identify suitable solutions, making the process both time-consuming and resource intensive. In this context, multi-objective Bayesian optimisation provides a clear advantage by intelligently navigating the design space, learning from each evaluation to reduce the number of simulations required, and efficiently addressing the complexities of non-linear material behaviour. By integrating finite element analysis with Bayesian optimisation, the framework developed in this study tackles the dual challenge of improving energy absorption and reducing peak force, particularly in scenarios where plastic deformation plays a critical role. The use of scalarisation and hypervolume-based techniques enables the identification of Pareto-optimal solutions, balancing these conflicting objectives.

翻译：在设计更安全、更高效的能量吸收结构时，工程师面临着提升压溃性能并平衡多个冲突目标（如最大化能量吸收和最小化峰值冲击力）的挑战。为精确模拟真实工况，需要使用复杂的材料模型来复现材料在冲击下的非线性行为，这带来了巨大的计算成本。本研究通过引入一个专门为优化自旋体结构压溃能量吸收而开发的多目标贝叶斯优化框架来解决这些挑战。自旋体结构以其可扩展、非周期性的拓扑结构和高效的应力分布特性，为先进结构设计提供了有前景的方向。然而，在真实工况下，优化设计参数以提升压溃性能远非易事。传统优化方法虽有效，但通常需要大量昂贵的仿真来寻找合适解，导致过程耗时且资源密集。在此背景下，多目标贝叶斯优化展现出显著优势：它能智能探索设计空间，从每次评估中学习以减少所需仿真次数，并有效应对非线性材料行为的复杂性。通过将有限元分析与贝叶斯优化相结合，本研究开发的框架解决了提升能量吸收与降低峰值力的双重挑战，尤其在塑性变形起关键作用的场景中。标量化与基于超体积的技术应用使得帕累托最优解的识别成为可能，从而平衡了这些冲突目标。