FMSG: Additive Manufacturing via Bioinspired Distributed Agents

FMSG：通过仿生分布式代理进行增材制造

• 批准号: 2036881
• 负责人: Jordan Raney
• 金额: $ 50万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036881&HistoricalAwards=false
• 关键词: FMSG; Additive; Manufacturing; via; Bioinspired

Production of complex structures via a swarm of simple, distributed agents is ubiquitous in biology (e.g., ant colonies, cellular formation of tissues), yet technological limitations have traditionally prevented this approach in manufacturing. In contrast, traditional manufacturing, from machining and casting to modern approaches such as roll-to-roll or additive manufacturing, require a deterministic plan which is executed in a series of precise, sequential steps. With miniaturization of robotics and novel inverse design tools, it is now feasible that the distributed, swarm approach used in nature could become an essential future manufacturing platform. The advantages of this approach include (1) the ability to produce complex multimaterial, multiscale structures without predetermined steps and (2) extreme robustness to manufacturing errors. This Future Manufacturing Seed Grant (FMSG) will establish the theoretical foundation and the practical tools necessary to pave the way for this nascent area of research and to understand its feasibility. In addition to the technical aspects of this work, this research could lead to a new area of manufacturing with beneficial economic and workforce implications. In conjunction with mid-sized and large manufacturing firms the researchers will therefore seek insight about how this new manufacturing paradigm would require changes to manufacturing curricula for the training and retraining of future manufacturing workers. This project will lead to the training of diverse graduate and undergraduate students and to outreach to K-12 students and the general public. The objective of this seed grant is to provide the framework and foundation for a new paradigm for future manufacturing in which multiple simple agents operate based on local information (e.g., temperature, light, pre-existing structure) to modify or manufacture complex structures based on simple local design rules. The feasibility of this new approach will be examined, and suitable vocabulary, processing formalization, design rules, etc., will be developed to establish this paradigm as a new, transformational area of research. The work will address the following key questions: How do local design rules map to different structural features, and ultimately to global mechanical properties (e.g., stiffness, strength, mass, Poisson's ratio) and combinations of properties (e.g., maximal specific toughness for a structure of minimal mass)? How do different design rules affect the rate of convergence toward optimal properties? The answers to these questions will also be incorporated in an inverse design framework, allowing local rules to be generated based on the desired properties. This grant is co-funded by the division of Civil, Mechanical and Manufacturing Innovation (CMMI) and the division of Industrial Innovation and Partnerships (IIP).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.

通过一群简单的、分布式的试剂生产复杂结构在生物学中普遍存在(例如蚂蚁群体、组织的细胞形成)，但传统上技术限制阻碍了这种方法在制造中的应用。相比之下，传统制造业，从机械加工和铸造到现代方法，如卷到卷或附加制造，都需要一个确定性的计划，该计划以一系列精确的、连续的步骤执行。随着机器人技术的小型化和新的逆向设计工具的出现，在自然界中使用的分布式群体方法现在可能成为未来必不可少的制造平台。这种方法的优点包括(1)能够制造复杂的多材料、多尺度结构，而无需预定的步骤；(2)对制造误差具有极强的稳健性。这项未来制造业种子基金(FMSG)将建立必要的理论基础和实用工具，为这一新兴领域的研究铺平道路，并了解其可行性。除了这项工作的技术方面，这项研究还可能带来一个新的制造业领域，具有有益的经济和劳动力影响。因此，研究人员将与中型和大型制造企业一起，深入了解这一新的制造模式将如何要求改变制造课程，以培训和再培训未来的制造业工人。该项目将对不同的研究生和本科生进行培训，并与K-12学生和普通公众进行接触。这项种子基金的目的是为未来制造的新范式提供框架和基础，其中多个简单代理基于本地信息(例如，温度、光线、预先存在的结构)进行操作，以基于简单的本地设计规则来修改或制造复杂的结构。将审查这一新方法的可行性，并将开发适当的词汇、处理形式化、设计规则等，以将这一范式确立为一个新的、变革性的研究领域。这项工作将解决以下关键问题：局部设计规则如何映射到不同的结构特征，并最终映射到全局机械性能(例如，刚度、强度、质量、泊松比)和性能组合(例如，最小质量结构的最大比韧性)？不同的设计规则如何影响向最佳属性收敛的速度？这些问题的答案也将被合并到反向设计框架中，允许根据所需的属性生成本地规则。这笔资金由土木、机械和制造创新部门(CMMI)和工业创新与伙伴关系部门(IIP)共同资助。这一奖项反映了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。