Collaborative Research: Knowledge and Data-driven Design of Mechanical Metamaterials

传统上，材料是通过选择分子水平的组成和结构来设计的。随着越来越复杂的材料形成技术的出现，如增材制造，在微米尺度上重复的结构现在也被用来实现有效的整体性能;这些材料被称为“超材料”。超材料与传统的全致密材料相比，最明显的好处是重量和材料消耗，但也有一些反应是全致密聚合物根本无法实现的。特别是，聚合物的3D打印已经达到了质量，速度和尺寸的关键阈值，可以用于生产而不仅仅是原型制作。超材料的重复结构单元内的几何形状严重影响整体特性。确定最佳几何形状需要一个不同于致密材料的设计框架。这项工作将探索将专家知识（即，物理定律，模型，物理学）和实际和模拟材料行为的数据库，使用先进的机器学习和搜索算法来促进具有所需特性的超材料的发现。该项目的进展将促进数据驱动设计的新领域，并通过促进具有迄今为止未知但理想的性能组合的先进材料的设计来促进国民健康，繁荣和福利。除了这种技术的影响，这笔赠款将有助于为下一代学生准备一个智能材料和结构设计的新时代。博士生、本科生、高中生和中学生将通过实验室研究经验和设计推广活动进行接触。这项工作的中心目标是创建一种利用现有工程知识和数据的3D打印弹性体超材料的设计方法。感兴趣的设计空间将包括两个不同的几何类-晶格材料和最小能量表面。本项目中的方法将利用基于物理的模型、现有知识和数据，最大限度地减少达到可接受设计所需的资源。中期研究目标是：（1）制定和验证一套全面的低计算成本力学模型，用于晶格和最小表面能类型的超材料，以及一套设计此类材料的方法;（2）开发数据驱动的替代模型，并确定3D打印机械超材料的预测机械性能的不确定性来源并量化。（3）开发知识表示和数据融合策略，将包括物理定律、物理学和信念在内的专家知识融入3D打印超材料的设计中。与当前最先进的超材料设计相比，该资助产生的设计框架将很好地适应大变形。这将有助于设计打印的超材料的属性，如韧性和故障应变。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的知识价值和更广泛的影响审查标准的支持。

Traditionally, materials have been designed through choices of molecular level composition and structure. With the advent of increasingly sophisticated material-forming techniques like additive manufacturing, structures repeated at microscale are also now being used to realize effective overall properties; these materials are termed "metamaterials". The most obvious gain for metamaterials versus traditional fully dense materials is in weight and material consumption, but there are also responses that are simply not achievable with fully dense polymers. In particular, 3D printing of polymers has reached a critical threshold of quality, speed, and size at which it can be used for production rather than just prototyping. The geometry within the repeated structural cell of a metamaterial critically influences the overall properties. Determining the optimal geometry requires a design framework distinct from that used for dense materials. This work will explore innovative ways of combining expert knowledge (i.e., physical laws, models, heuristics) and databases of actual and simulated material behaviors, using advanced machine learning and search algorithms to foster the discovery of metamaterials with desired properties. Progress in the project will promote the new field of data-driven design as well as advance the national health, prosperity, and welfare by facilitating the design of advanced materials with hitherto unknown, yet desirable combination of properties. Beyond this technological impact, this grant will serve to prepare the next generation of students for a new era of design for intelligent materials and structures. Doctoral, undergraduate, high school, and middle school students will be reached through in-lab research experiences and design outreach activities.The central objective of this work is to create a design method for 3D printable elastomeric metamaterials that leverages both available engineering knowledge and data. The design space of interest will include two distinct geometry classes -- lattice materials and minimum energy surfaces. The methodology in this project will leverage physics-based models, existing knowledge, and data to minimize the resources needed to reach an acceptable design. The intermediate research objectives are to: (1) formulate and validate a comprehensive set of low computational cost mechanics models for lattice and minimum surface energy style metamaterials, together with a set of heuristics for designing such materials; (2) develop data-driven surrogate models and identify sources of and quantify uncertainty in predicted mechanical properties of 3D printed mechanical metamaterials; (3) develop knowledge representations and data fusion strategies to incorporate expert knowledge including physical laws, heuristics, and beliefs into the design of 3D printed metamaterials. In contrast to the current state-of-the-art for metamaterial design, the design framework that is produced by this grant will be well oriented to accommodate large deformation. This will facilitate design of printed metamaterials for properties such as toughness and failure strain.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.