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

由于其非凡的性能，工程超材料是不同行业的广泛功能产品的基础，如材料和能源。这一计算和数据支持的科学与工程(CDS&Amp；E)合作研究奖将为可制造机械超材料的发现和优化建立一种数据驱动的方法，通过利用人工智能的探索和外推能力来共同设计用于超轻型能源设备和形状变形软机器人等产品的非周期性蜂窝材料的几何和属性，从而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。将研究成果整合到教育活动中，将有助于对学生进行数据科学、工程设计和先进制造方面的培训，扩大代表不足的少数群体和第一代大学生参与设计和3D打印研究和教育的范围。该研究弥合了对三维非周期结构蜂窝材料(AACM)结构-性能关系的基本理解的知识鸿沟，实现了具有期望性能的可添加制造蜂窝材料的逆向设计。该项目将通过利用数据驱动的方法，为具有特定属性的可添加制造的蜂窝材料建立一个合理的设计范例。它将解决由非常大的几何空间、未知的属性空间的理论极限、病态逆问题以及几何兼容性和可制造性约束所带来的挑战。研究工作包括：(1)通过计算发现框架扩展AACM单元力学性质空间的理论极限；(2)阐明元胞结构的几何-性质关系，推导出计算高效的数据驱动逆映射，以生成具有指定性质的不同AACM结构；(3)考虑非周期结构图案组合设计中的兼容性和可加性制造挑战。对内在结构-制造-性能关系的深入理解将促进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。

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.