Marco Wurster
Papers
5
Total Citations
80
H-Index
4
About
Marco Wurster is a leading researcher in remanufacturing and intelligent production systems, with a focus on tackling the high uncertainty inherent in processing used products. His core work centers on developing agile, changeable production systems that can adapt to unknown product conditions, particularly through the integration of reinforcement learning and cognitive robotics. His most influential paper, "Modelling and condition-based control of a flexible and hybrid disassembly system... using reinforcement learning" (2022, 51 citations), introduces a novel framework for controlling disassembly lines that combine manual and autonomous workstations, directly addressing a critical gap in production planning and control. Wurster further advanced the field by coining the concept of "Fluid Automation" (2021, 15 citations), defining a new paradigm for adaptive remanufacturing systems. His work also explores cognitive factories, where robots learn and transfer knowledge across system entities, and simulation-based trajectory planning for robotic inspection. By bridging machine learning, robotics, and sustainable manufacturing, Wurster’s research provides foundational solutions for making remanufacturing economically viable and operationally efficient, positioning him as a key contributor to the future of circular production.
Research Focus
Key Achievements
Top Papers
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