Nature Communications ( IF 14.7 ) Pub Date : 2023-09-18 , DOI: 10.1038/s41467-023-40854-1
Chan Soo Ha 1 , Desheng Yao 2, 3 , Zhenpeng Xu 2, 3 , Chenang Liu 4 , Han Liu 5 , Daniel Elkins 1, 6 , Matthew Kile 1 , Vikram Deshpande 7 , Zhenyu Kong 6 , Mathieu Bauchy 3 , Xiaoyu Rayne Zheng 1, 2, 3
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Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles.
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通过机器学习根据规定的机械行为快速逆向设计超材料
设计和打印具有可定制架构的超材料可以实现超越其组成材料的前所未有的机械行为。这些行为以响应曲线的形式记录,应力-应变曲线描述了它们的准静态足迹。然而,由于多个设计目标、非线性行为和工艺相关的制造误差带来的挑战,现有的逆向设计方法尚未成熟,无法捕获全部所需的行为。在这里,我们报告了一种快速逆向设计方法,利用生成机器学习和桌面增材制造,该方法能够创建几乎所有可能的单轴压缩应力应变曲线案例,同时考虑打印过程中的工艺相关错误。结果表明,可以实现完全可定制的机械行为,目标结果与实验测量结果之间的保真度接近 90%。我们的方法代表了逆向设计材料的起点,该材料满足规定但复杂的行为,并可能绕过迭代设计-制造周期。