QRM: Understanding the Mechanical Design of Natural Cellular Materials via a Multiscale Quantitative Structural Representation

QRM：通过多尺度定量结构表示理解天然细胞材料的机械设计

• 批准号: 1825646
• 负责人: Ling Li
• 金额: $ 53.59万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825646&HistoricalAwards=false
• 关键词: QRM; Understanding; Mechanical; Design; Natural

Many biological materials systems, such as wood, bones, sponges, and sea urchins, utilize cellular structures for simultaneous mechanical function and weight saving. Similarly, engineering cellular solids with lightweight design and mechanical efficiency, have become increasingly favored for applications in energy, infrastructure, aerospace, biomedical, and the automotive vehicle industries. While many engineering cellular materials are manufactured to have either completely stochastic or periodic lattices, natural cellular materials often exhibit complex three-dimensional cellular designs with controlled structural morphologies at multiple length scales. This is believed to contribute to their remarkable mechanical robustness, especially considering their mechanically weak or soft constituents. This award will support a fundamental study to elucidate the multi-scale structural basis of the high-performance natural cellular materials, through an integrated multi-disciplinary effort involving materials science and engineering, mechanical engineering, computer vision, and data science. The insights gained from natural cellular materials will provide important guidance in the design and manufacturing of engineering cellular materials, deepen our understanding of structural cellular solids, and provide insights for the design and fabrication of advanced lightweight materials and structures, with potential benefits to US Manufacturing and Infrastructure sectors.This work will establish a quantitative, comprehensive, and efficient structural representation scheme for the multi-scale cellular network of highly porous structures found in natural systems. The researchers will study the bioceramic cellular structure of sea urchin spines as a testbed system, and multiscale structural representations will elucidate fundamental understanding of their remarkable mechanical robustness and damage tolerance. The work will address the challenges of representing complex, hierarchical cellular structures via three major innovations, including high-resolution tomography imaging and adaptive compressive data processing, multi-scale representation of the cellular structure (individual strut and node level, local unit-cell level, and global cellular network level), and in-silico "regrowth" of bio-inspired cellular structures with morphological control at multiple length scales. The research team will further validate the multi-level cellular structural representation through systematic finite element simulations coupled with synchrotron imaging-based in-situ mechanical testing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多生物材料系统，如木材、骨骼、海绵和海胆，利用细胞结构同时实现机械功能和减轻重量。同样，具有轻量化设计和机械效率的工程蜂窝固体在能源、基础设施、航空航天、生物医学和汽车工业中的应用也越来越受欢迎。虽然许多工程蜂窝材料被制造成具有完全随机或周期性的晶格，但天然蜂窝材料通常表现出复杂的三维蜂窝设计，其结构形态在多个长度尺度上可控。这被认为有助于它们非凡的机械坚固性，特别是考虑到它们的机械组成较弱或较软。该奖项将支持一项基础性研究，通过涉及材料科学和工程、机械工程、计算机视觉和数据科学的综合多学科努力，阐明高性能天然蜂窝材料的多尺度结构基础。从天然细胞材料中获得的见解将为工程细胞材料的设计和制造提供重要指导，加深我们对结构细胞固体的理解，并为先进的轻质材料和结构的设计和制造提供见解，这将为美国制造业和基础设施部门带来潜在的好处。这项工作将为自然系统中发现的高度多孔结构的多尺度细胞网络建立一个定量、全面和高效的结构表示方案。研究人员将研究海胆脊椎的生物陶瓷细胞结构作为试验台系统，多尺度结构表示将阐明对其非凡的机械稳健性和损伤耐受性的基本理解。这项工作将通过三项主要创新来解决表示复杂的、分层的细胞结构的挑战，包括高分辨率断层成像和自适应压缩数据处理、细胞结构的多尺度表示(单个支柱和节点级、局部单元细胞级和全球蜂窝网络级)，以及在多个长度尺度上通过形态控制来实现生物启发的细胞结构的电子“再生”。研究团队将通过系统的有限元模拟和基于同步辐射成像的现场力学测试来进一步验证多层次细胞结构表示。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Tracing plastic deformation path and concurrent grain refinement during additive friction stir deposition

在添加剂搅拌摩擦沉积过程中追踪塑性变形路径和并发晶粒细化

• DOI: 10.1016/j.mtla.2021.101159
• 发表时间: 2021
• 期刊: Materialia
• 影响因子: 3.4
• 作者: Perry, Mackenzie E.J.;Rauch, Hunter A.;Griffiths, R. Joey;Garcia, David;Sietins, Jennifer M.;Zhu, Yunhui;Zhu, Yuntian;Yu, Hang Z.
• 通讯作者: Yu, Hang Z.

Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning

• DOI: 10.1016/j.addma.2020.101183
• 发表时间: 2020-04
• 期刊: Additive manufacturing
• 影响因子: 11
• 作者: Yunhui Zhu;Ziling Wu;W. Douglas Hartley;J. Sietins;Christopher B. Williams;Hang Z. Yu

Quantitative 3D structural analysis of the cellular microstructure of sea urchin spines (II): Large-volume structural analysis

• DOI: 10.1016/j.actbio.2020.03.006
• 发表时间: 2020-04-15
• 期刊: ACTA BIOMATERIALIA
• 影响因子: 9.7
• 作者: Chen, Hongshun;Yang, Ting;Li, Ling
• 通讯作者: Li, Ling

Automatic Crack Detection and Analysis for Biological Cellular Materials in X-Ray In Situ Tomography Measurements

• DOI: 10.1007/s40192-019-00162-3
• 发表时间: 2019-11
• 期刊: Integrating Materials and Manufacturing Innovation
• 影响因子: 3.3
• 作者: Ziling Wu;Ting Yang;Zhifei Deng;Baokun Huang;Han Liu;Yu Wang;Yuan Chen;M. Stoddard;Ling Li;Yunhui Zhu

Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning

• DOI: 10.1557/jmr.2020.120
• 发表时间: 2020-06
• 期刊: Journal of Materials Research
• 影响因子: 2.7
• 作者: Yi Han;R. J. Griffiths;Hang Z. Yu;Yunhui Zhu