Data-driven modelling of scalable spinodoid structures for energy absorption

H. Kansara, G. Koh, M. Varghese, John Z. X. Luk, E. Gómez, Siddhant Kumar, Han Zhang, Emilio Mart'inez-Paneda, Wei Tan
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引用次数: 1

Abstract

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/m3 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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可伸缩棘突结构的能量吸收数据驱动建模
该项目旨在探索一种设计和生产具有良好吸能性和可恢复性的细胞材料的新方法。spininodoid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,可确保在更大范围内实现微观效益。通过增材制造,各种材料和拓扑结构已被用于数值建模和原型设计。每个设计都使用有限元建模进行评估。数值模型的初步结果表明,各向异性结构具有较高的能量吸收效率。通过数据驱动优化,各向异性柱状结构的峰值能量吸收值为5.34 MJ/m3。由于并行探索参数,物理知情的有偏差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。
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