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