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Automation dominates modern factories, but much of it still breaks when parts vary, damage is uncertain, and expert judgement is required. Farhad Imani’s project targets this failure by developing robotic manufacturing systems that can sense change and adapt in real time.
A critical challenge is emerging in manufacturing: how to repair and restore high-value components when current systems can’t handle deviation. Factories are full of automation systems that perform well when processes are repetitive. The moment geometry shifts, the process changes, or defects evolve, they struggle.
NSF CAREER Award recipient Farhad Imani, an assistant professor in mechanical engineering at the University of Connecticut, is tackling this challenge head-on through the development of a new class of intelligent robotic manufacturing systems that can inspect parts, interpret multimodal sensor data, and reason through uncertainty.
Read more in the UConn Today article.