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

由于其非凡的特性，工程超材料是材料和能源等不同行业领域各种功能产品的基础。这个计算和数据支持的科学与工程（CDS& E）合作研究奖将建立一个数据驱动的方法，用于可制造的机械超材料发现和优化，通过利用人工智能的探索和外推能力来实现先进建筑材料的全部潜力。帮助振兴美国的先进制造业。将研究成果融入教育活动将有助于培养数据科学，工程设计和先进制造的学生，扩大少数民族和第一代大学生在设计和3D打印研究和教育中的参与。这项研究填补了对三维非周期性结构蜂窝材料（AACM）结构-性能关系的基本理解的知识空白。以及实现具有所需性质的可增材制造的多孔材料的逆向设计。该项目将通过利用数据驱动的方法，为具有特定属性的增材制造蜂窝材料建立一个合理的设计范例。它将解决一个非常大的几何空间，未知的属性空间的理论限制，不适定的逆问题，几何兼容性和可制造性的限制所带来的挑战。研究活动包括：（1）通过计算发现框架扩展AACM单元的机械性能空间的理论极限;（2）阐明细胞结构的几何-性能关系，以导出用于生成具有规定性能的不同AACM结构的计算有效的数据驱动的逆映射;（3）尊重非周期性结构图案的组合设计中的兼容性和增材制造性挑战。对内在结构-制造-性能关系的深入理解将推动新型建筑材料设计和开发的基础研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.