CDS&E/Collaborative Research: Data-Driven Inverse Design of Additively Manufacturable Aperiodic Architected Cellular Materials

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Due to their extraordinary properties, engineered metamaterials are the basis for a wide range of functional products across different industry sectors, such as materials and energy. This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research award will establish a data-driven approach for manufacturable mechanical metamaterials discovery and optimization to realize the full potential of advanced architected materials by harnessing the exploration and extrapolation capability of artificial intelligence for the co-design of the geometry and properties of aperiodic cellular materials used in products such as ultra-light energy devices and shape-morphing soft robotics, helping to revitalizing advanced manufacturing in the US. Integrating the research findings into educational activities will help train students in data science, engineering design, and advanced manufacturing, broadening the participation of underrepresented minorities and first-generation college students in design and 3D printing research and education.This research bridges the knowledge gap in the fundamental understanding of the structure-property relation of three-dimensional aperiodic architected cellular materials (AACM) and achieving the inverse design of additively manufacturable cellular materials with desired properties. This project will establish a rational design paradigm for additively manufacturable cellular materials with specified properties by leveraging data-driven approaches. It will address the challenges posed by a very large geometry space, unknown theoretical limits of the property space, ill-posed inverse problems, and geometric compatibility and manufacturability constraints. The research activities include: (1) extending the theoretical limits of mechanical property space of AACM units via a computational discovery framework; (2) elucidating the geometry-property relation of cellular structures to derive a computationally efficient data-driven inverse mapping for generating diverse AACM structures with prescribed properties; (3) respecting the compatibility and additive manufacturability challenges in the combinatorial design of aperiodic structural patterns. The enhanced understanding of intrinsic structure-manufacturing-property relation will advance fundamental research of novel architected materials design and development.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.