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

FMSG：通过机器人工作单元的工具和控制的协同设计，快速重新利用和扩展装配线

• 批准号: 2035717
• 负责人: Adriana Schulz
• 金额: $ 50万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035717&HistoricalAwards=false
• 关键词: FMSG; Rapidly; Repurposing; Scaling; Assembly

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.

该项目允许重新使用制造生产线的重新使用和重新缩放。典型的制造装配线由一系列工作单元组成，每个序列都需要执行不同过程的不同机制。在此框架中的自动生产通常不灵活 - 生产的变化需要耗时的重新设计和昂贵的组件重新编程。结果，自动化的装配线生产无法响应市场需求的快速变化。这不仅限制了产品更新的速度和对市场动态的响应，而且还会阻碍制造商在国家紧急情况下在国家紧急情况下迅速做出反应，而这种灵活性对于国家反应至关重要。该项目改变了如何在工厂地板上快速更新组件的方式，重点是两个特定但广泛使用的自动化任务，使组装线更加聪明，对不断变化的产品线的生产更加敏感，并为“制造作为服务”的目标做出了贡献。该项目开发了一种有效的方法来共同设计特定于任务的工具和控制，该方法通常适用于制造系统，将数字制造与数字组件团结起来。该项目将两个组装任务（重新定位和插入）重点放在，并将其作为网络物理子系统的工具和控制共同设计。该项目借鉴了机器人技术，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来进行对象操纵，开发算法家庭，用于使用人工智能和机器学习中的技术来共同设计工具和控制的方法。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识绩效和更广泛的影响审查的审查标准来通过评估来通过评估来支持的。

项目成果

Computational design of passive grippers

被动夹具的计算设计

• DOI: 10.1145/3528223.3530162
• 发表时间: 2022
• 期刊: ACM Transactions on Graphics
• 影响因子: 6.2
• 作者: Kodnongbua, Milin;Good, Ian;Lou, Yu;Lipton, Jeffrey;Schulz, Adriana
• 通讯作者: Schulz, Adriana