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About

Mateusz Zwierzycki is a pioneer at the intersection of robotic fabrication and machine learning, with a primary focus on advancing incremental sheet metal forming. His research confronts a fundamental challenge in digital fabrication: the unpredictable, variable behavior of industrial metals. Rather than treating material inconsistency as a flaw, Zwierzycki’s work harnesses adaptive computational techniques to turn it into a controllable design parameter. His most cited paper, “Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming” (28 citations), demonstrates how machine learning can predict and correct geometric deviations in real time, enabling the production of freeform metal panels with unprecedented accuracy. In a companion study, “Adaptive Robotic Fabrication for Conditions of Material Inconsistency” (12 citations), he extends this logic into a fully integrated cyber-physical workflow, where sensors and algorithms continuously adjust robotic toolpaths to compensate for material springback and thickness variations. Beyond metal forming, Zwierzycki has also contributed to high-resolution simulation of braiding patterns (8 citations), expanding computational design into textile structures. His work, often developed in collaboration with leading researchers at the Royal Danish Academy and Fabricate, has been instrumental in bridging the gap between digital design intent and physical material reality, establishing a new paradigm for adaptive, intelligent manufacturing.