FMSG: Rapidly Repurposing and Scaling Assembly Lines Through the Co-design of Tooling and Controls of Robotic Work Cells

该项目允许制造生产线的重新利用和重新扩展，以快速和经济实惠的方式完成。典型的制造装配线由一系列工作单元组成，每个工作单元需要不同的机制来执行不同的过程。在这种框架下的自动化生产通常是不灵活的-生产中的变化需要耗时的重新设计和昂贵的组件重新编程。因此，自动化流水线生产无法应对市场需求的快速变化。这不仅限制了产品更新和对市场动态作出反应的速度，而且也阻碍了制造商在国家紧急情况下迅速作出反应，而这种灵活性对国家的反应至关重要。该项目改变了装配线在工厂车间快速更新的方式，专注于两个特定但广泛使用的自动化任务，使装配线更加智能化，并对不断变化的产品线的生产做出响应，并为“制造即服务”的目标做出贡献。“该项目开发了一种有效的方法来协同设计特定任务的工具和控制，一般适用于制造系统，将数字制造与数字装配相结合。该项目的重点是两个装配任务，重新定位和插入，与他们的共同设计的工具和控制作为网络物理子系统。 该项目借鉴了机器人、控制器优化、算法设计、软件和硬件、材料科学、机械工程以及制造科学和工程的学科和技术。 该项目采用基于模型的数据驱动方法进行对象操作，使用人工智能和机器学习技术开发用于工具和控制协同设计的算法系列。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project allows re-purposing and re-scaling of manufacturing production lines to be done quickly and affordably. A typical manufacturing assembly line is composed of a sequence of work cells, each requiring different mechanisms to execute different processes. Automated production in this framework is often inflexible – a change in the production requires the time-consuming re-design and expensive reprogramming of components. As a result, an automated assembly line production is unable to respond to rapid changes in market needs. This not only limits the speed of product updates and response to market dynamics, but also impedes manufacturers from reacting quickly during a national emergency when that flexibility is essential for a national response. This project changes how assembly lines can be rapidly updated on the factory floor, focusing on two specific but widely-used automated tasks, making assembly lines more intelligent and responsive to the production of changing products lines, and contributing to the goal of "manufacturing as a service."This project develops an efficient approach to co-design of task-specific tooling and control, applicable in general for manufacturing systems, uniting digital fabrication with digital assembly. The project focuses two assembly tasks, re-orientation and insertion, with their co-design of tooling and controls as cyber-physical sub-systems. The project draws upon the disciplines and technologies for robotics, controller optimization, algorithmic design, software and hardware, material science, mechanical engineering, and manufacturing science and engineering. The project takes a model-based data-driven approach to object manipulation, developing families of algorithms for co-design of tooling and controls, using techniques from artificial intelligence and machine learning.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.