Cornelia Tepper

Papers

Total Citations

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About

Cornelia Tepper is a leading researcher in industrial robotics, specializing in the intersection of compliance modeling, optimal design of experiments, and hybrid production systems. Her work addresses critical challenges in robotic precision and manufacturing efficiency. Tepper’s most notable contribution is a cost- and time-efficient methodology for modeling industrial robot compliance using Bayesian inference of gear stiffness parameters, published in 2023. This approach, which has garnered 7 citations, enables rapid tuning of stiffness models through an optimal experimental design, significantly enhancing robotic accuracy for tasks like machining and assembly. Her earlier 2019 paper on a robot-based hybrid production concept, with 5 citations, explores the integration of robots into flexible manufacturing systems, blending additive and subtractive processes. Tepper’s research directly impacts industries seeking to improve robot performance without expensive hardware upgrades, making her work essential for engineers and researchers advancing smart manufacturing. Her innovative methods for compliance modeling and production optimization position her as a key figure in the evolution of adaptive, high-precision robotic systems.