CAREER: Automated Synthesis of Compound Machines Using Computational Design Optimization

职业：使用计算设计优化自动合成复合机器

• 批准号: 2311078
• 负责人: Kai James
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311078&HistoricalAwards=false
• 关键词: CAREER; Automated; Synthesis; Compound; Machines

Computer algorithms have been used to automatically generate and optimize designs for everything from aircraft wing structures to cardiovascular stents. However, these algorithms are typically limited to the design of single-body structures, with all motion occurring by means of structural deformation. This limits the complexity of the designs that can be synthesized, preventing the design of multi-body systems containing several moving parts. This Faculty Early Career Development Program (CAREER) project will advance the science of automated computational design by creating new algorithms capable of generating and optimizing multi-body systems. Starting with only a mathematical description of design materials and physical environment, the algorithms will automatically generate assemblies for compound machines that combine multiple basic components such as levers, hinges, wheels, and axles, which together form the foundation of all mechanical design. This work will enable a new level of automated computational design not previously possible under existing design frameworks. The knowledge obtained from this research will benefit a wide range of applications including nanoscale mechanisms for delivery of medications and self-reproducing robotic systems. The project also involves an integrated education program featuring a STEM Pipeline program for undergraduate students from underrepresented minority groups. The program includes bi-weekly labs and seminars, team design projects, and K-12 outreach activities. The algorithms created in this project will rely upon several novel design formulations devised specifically for the design task. Chief among these will be an original topology optimization-type framework in which the internal topologies of multiple planar design domains are optimized simultaneously, while also optimizing the connectivity between adjacent domains to form compound mechanisms whose components can slide and rotate freely with respect to one another. The mechanical behavior of the designs will be modeled using a combination of finite element analysis and flexible multibody dynamics to capture the rigid body motion and the elastic deflection of the system components. Additionally, adjoint sensitivity analysis will be used to derive and compute the design sensitivities that will power the gradient-based optimization algorithms. The new design framework will be validated via high-fidelity computational simulations, as well as through fabrication and experimental testing. This testing will be used to quantify the efficiency of the generated designs, as determined by their mechanical and geometric advantage.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.

计算机算法已用于自动生成和优化从飞机机翼结构到心血管支架的所有事物的设计。 但是，这些算法通常仅限于单体结构的设计，所有运动都通过结构变形进行。 这限制了可以合成的设计的复杂性，从而阻止了包含多个运动部件的多体系统的设计。 这个教师早期职业发展计划（职业）项目将通过创建能够生成和优化多体系统的新算法来推动自动计算设计的科学。 从设计材料和物理环境的数学描述开始，该算法将自动生成组装组件，这些机器结合了多个基本组件，例如杠杆，铰链，车轮和轴，这些组件共同构成了所有机械设计的基础。这项工作将使以前在现有设计框架下无法实现新的自动化计算设计水平。 从这项研究中获得的知识将受益于广泛的应用，包括用于输送药物和自我产生的机器人系统的纳米级机制。该项目还涉及一项综合教育计划，该计划为来自代表性不足的少数群体的本科生提供STEM管道计划。该计划包括每两周一次的实验室和研讨会，团队设计项目和K-12外展活动。该项目中创建的算法将依赖于专门为设计任务设计的几种新型设计配方。 其中最主要的是原始拓扑优化型框架，在该框架中，多个平面设计域的内部拓扑同时优化，同时还优化了相邻域之间的连通性以形成复合机制，其组件可以互相滑动并互相自由旋转。 设计的机械行为将使用有限元分析和柔性多体动力学的组合来建模，以捕获刚体运动和系统组件的弹性偏转。 此外，伴随灵敏度分析将用于得出和计算将为基于梯度的优化算法提供动力的设计灵敏度。 新的设计框架将通过高保真计算模拟以及通过制造和实验测试来验证。 该测试将用于量化生成的设计的效率，这取决于它们的机械和几何优势。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。