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

CDS

• 批准号: 2245299
• 负责人: Jun Wang
• 金额: $ 24.98万
• 依托单位: Santa Clara University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245299&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Data; Driven

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.

由于其非凡的特性，工程化的超材料是跨不同行业（例如材料和能源）各种功能产品的基础。 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机器人技术，有助于振兴美国的先进制造业。 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具有所需特性的可制造的蜂窝材料。该项目将通过利用数据驱动的方法来建立具有可添加性制造的蜂窝材料的合理设计范式。它将解决一个非常大的几何空间，财产空间的未知理论限制，不适合的逆问题以及几何兼容性和制造性约束所带来的挑战。研究活动包括：（1）通过计算发现框架扩大AACM单位机械性能空间的理论限制； （2）阐明细胞结构的几何形状 - 特质关系，以得出计算有效的数据驱动的逆映射，以生成具有规定属性的不同AACM结构； （3）尊重在多种结构模式组合设计中的兼容性和加性生产性挑战。对内在结构 - 制造业关系的增强理解将推进对新型构建材料设计和开发的基本研究。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估标准来通过评估来获得支持的。