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各种设计。 然而，这些算法通常仅限于单体结构的设计，所有运动都是通过结构变形发生的。 这限制了可综合设计的复杂性，阻碍了包含多个运动部件的多体系统的设计。 该教师早期职业发展计划（CAREER）项目将通过创建能够生成和优化多体系统的新算法来推进自动化计算设计科学。 仅从设计材料和物理环境的数学描述开始，算法将自动生成复合机器的组件，这些组件结合了多个基本组件，例如杠杆、铰链、轮子和车轴，这些组件共同构成了所有机械设计的基础。这项工作将使自动化计算设计达到一个新的水平，这在现有的设计框架下是不可能实现的。 从这项研究中获得的知识将有利于广泛的应用，包括用于输送药物的纳米级机制和自我复制的机器人系统。该项目还涉及一项综合教育计划，其中包括针对代表性不足的少数群体的本科生的 STEM 管道计划。该计划包括每两周一次的实验室和研讨会、团队设计项目以及 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颖的设计公式。 其中最主要的是一种原创的拓扑优化型框架，其中多个平面设计域的内部拓扑同时优化，同时优化相邻域之间的连接性，以形成其组件可以相对于彼此自由滑动和旋转的复合机构。 将结合有限元分析和柔性多体动力学对设计的机械行为进行建模，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导出和计算设计灵敏度，为基于梯度的优化算法提供动力。 新的设计框架将通过高保真计算模拟以及制造和实验测试进行验证。 该测试将用于量化所生成设计的效率，具体取决于其机械和几何优势。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。