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.
