论文标题
数据驱动的准定序机械超材料的发现逐渐失败
Data-driven discovery of quasi-disordered mechanical metamaterials failed progressively
论文作者
论文摘要
天然细胞材料,例如蜂窝,木材,泡沫,小梁骨,植物实质和海绵,可能会受益于其内部微观结构内的混乱,以实现耐受的行为。受此启发,我们通过引入空间坐标扰动或支撑厚度变化来创建了准序列的桁架超材料(QTM)(QTMS)。数值研究表明,QTM可以表现出延性,耐受的行为或突然的灾难性失败模式,具体取决于引入的混乱性的分布。已经开发了一种数据驱动的方法,结合了深度学习和全球优化算法,以调整无效性的分布,以实现耐受的QTM设计。一项关于由周期性脸部立方体(FCC)晶格创建的QTM的案例研究表明,优化的QTM可以以低于5%的刚度和较小的拉伸强度来实现高达100%的延展性。我们的结果表明,建筑材料的新设计途径可以提高损伤耐受性。
Natural cellular materials, such as honeycombs, woods, foams, trabecular bones, plant parenchyma, and sponges, may benefit from the disorderliness within their internal microstructures to achieve damage tolerant behaviours. Inspired by this, we have created quasi-disordered truss metamaterials (QTMs) via introducing spatial coordinate perturbations or strut thickness variations to the perfect, periodic truss lattices. Numerical studies have suggested that the QTMs can exhibit either ductile, damage tolerant behaviours or sudden, catastrophic failure mode, depending on the distribution of the introduced disorderliness. A data-driven approach has been developed, combining deep-learning and global optimization algorithms, to tune the distribution of the disorderliness to achieve the damage tolerant QTM designs. A case study on the QTMs created from a periodic Face Centred Cubic (FCC) lattice has demonstrated that the optimised QTMs can achieve up to 100% increase in ductility at the expense of less than 5% stiffness and less than 10% tensile strength. Our results suggest a novel design pathway for architected materials to improve damage tolerance.