Parameter Space Exploration of Cellular Mechanical Metamaterials Using Genetic Algorithms
Parameter Space Exploration of Cellular Mechanical Metamaterials Using Genetic Algorithms
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DOI：
10.2514/1.j062864
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发表时间：
2023-06
期刊：
影响因子：
2.5
通讯作者：
Sheng Liu;P. Acar
中科院分区：
文献类型：
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作者：
Sheng Liu;P. Acar
多孔材料广泛存在于天然生物系统中，如蜂巢、骨骼和木材。随着增材制造技术的进步，由于多孔超材料独特的力学性能，对其的研究不断涌现。然而，按需设计多孔超材料通常需要解决一个极具挑战性的逆向设计问题，即在设计领域中探索微观结构代表性体积单元（RVE）复杂的结构 - 性能关系。在此，我们提出一种无需经验的系统方法，利用多目标遗传算法（GA）探索多孔力学超材料微观结构的参数化系统。从全局来看，考虑到基于种群的启发式优化方法中初始种群选择的重要性，我们研究了不同采样方法初始化的种群对最优解的影响。从局部来看，我们通过使用带有新搜索策略的微遗传算法来改进我们的方法，该方法要求在全局搜索过程中，标准遗传算法以小种群规模有条件地运行足够次数。我们已将该方法应用于有效且准确地探索映射到具有周期性和非周期性RVE的多孔力学超材料两种不同参数空间应用的最优解。
Cellular materials widely exist in natural biologic systems such as honeycombs, bones, and woods. With advances in additive manufacturing, research on cellular metamaterials is emerging due to their unique mechanical performance. However, the design of on-demand cellular metamaterials usually requires solving a challenging inverse design problem for exploring complex structure–property relations of microstructured representative volume elements (RVEs) in the design domain. Here, we propose an experience-free and systematic methodology for exploring a parametrized system for microstructures of cellular mechanical metamaterials using a multiobjective genetic algorithm (GA). Globally, by considering the importance of the initial population selection for a population-based heuristic optimization method, we study the impact of the populations initialized by the different sampling methods on the optimal solutions. Locally, we develop our method by using a micro-GA with a new searching strategy, which requires the standard genetic algorithm to be conditionally run for a sufficient number of times with a small population size during the global searching process. We have applied our method to explore optimal solutions for applications mapped on two different parameter spaces of the cellular mechanical metamaterials with periodic and nonperiodic RVEs effectively and accurately.