Nicole Stricker
Papers
6
Total Citations
85
H-Index
4
About
Nicole Stricker is a leading researcher in the emerging field of remanufacturing production systems, where she tackles the critical challenge of restoring used products to like-new condition. Her work centers on developing flexible, hybrid disassembly systems that combine manual and autonomous workstations, with a particular focus on using reinforcement learning for condition-based control—a contribution that has earned her most-cited paper over 50 citations. Stricker has pioneered the concept of "Fluid Automation," defining adaptive mechanisms that allow production systems to rapidly respond to the high uncertainty inherent in remanufacturing, where incoming product conditions are unpredictable. She has also advanced cognitive manufacturing by developing models that enable robots to learn from their environment and transfer knowledge across system entities, moving beyond static, rule-based automation. Her research extends to automated inspection stations and simulation-based trajectory planning for laser-scanning robots, addressing the complex challenge of automating quality assessment in remanufacturing. With a growing body of work that bridges production planning, robotics, and artificial intelligence, Stricker is establishing herself as a key voice in making remanufacturing more efficient, agile, and sustainable.
Research Focus
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Top Papers
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