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About

Patrick Seim is a leading researcher in the field of advanced manufacturing, with a primary focus on robot-based incremental sheet metal forming. His work centers on solving one of the most persistent challenges in this domain: achieving high geometric accuracy in flexible, dieless forming processes. Seim’s major contributions include pioneering the use of machine learning to enhance precision in the ROBOFORMING process, a technique that employs two cooperating industrial robots to shape sheet metal components for small lot sizes and prototypes. His research has systematically investigated how factors like part orientation and the integration of stiffening elements can significantly improve dimensional fidelity. With his most-cited work, "Machine Learning In Incremental Sheet Forming" (7 citations), Seim has demonstrated how data-driven approaches can correct process deviations, while his subsequent studies (each with 5 citations) have provided foundational insights into process mechanics. By bridging the gap between computational intelligence and physical forming operations, Seim has established himself as a key innovator in making flexible sheet metal forming more reliable and industrially viable for low-volume production.