用于能量吸收的可扩展Spinodoid结构的数据驱动建模
Data-driven modelling of scalable spinodoid structures for energy absorption
摘要：
该项目旨在探索一种新的方法来设计和生产具有良好能量吸收和可回收性能的蜂窝材料。Spinodoid结构为蜂窝和泡沫等工程结构提供了一种替代方案，具有可扩展性，确保在更大规模上获得微尺度效益。各种材料和拓扑结构已被用于通过增材制造的数字建模和原型制作。使用有限元建模对每种设计进行评价。数值模型的初步结果表明，各向异性结构实现高能量吸收效率。通过数据驱动的优化，结果显示各向异性柱状结构的峰值能量吸收值为5.34 $MJ/m^{3}$。由于参数是并行探索的，因此基于物理信息的有偏网格搜索优化速度更快。为了验证数值模型，对各种原型进行了压缩试验。
英文摘要：
The project aims to explore a novel way to design and produce cellular materials with good energy absorption and recoverability properties. Spinodoid structures offer an alternative to engineering structures such as honeycombs and foam with scalability ensuring microscale benefits are reaped on a larger scale. Various materials and topologies have been utilised for numerical modeling and prototyping through additive manufacturing. Each design was evaluated using finite element modelling. Initial results from numerical models show anisotropic structures achieving high energy absorption efficiency. Through data-driven optimisation, results show a peak energy absorption value of 5.34 $MJ/m^{3}$ for anisotropic columnar structure. A physics-informed biased grid-search optimisation is faster due to parameters being explored in parallel. To validate the numerical model, compressive tests on various prototypes were conducted.
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