计算机科学
微尺度化学
有限元法
人工神经网络
承重
人工智能
材料科学
机械工程
结构工程
工程类
复合材料
数学教育
数学
作者
Bo Peng,Wei Ye,Yu Qin,Jiabao Dai,Yue Li,Aobo Liu,Yun Tian,Liuliu Han,Yufeng Zheng,Peng Wen
标识
DOI:10.1038/s41467-023-42415-y
摘要
Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts' prior knowledge and requires painstaking effort. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we apply our method to orthopedic implant design. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Thus, our method provides a data-efficient paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties.
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