Thomas Engbers
Papers
2
Total Citations
18
H-Index
2
About
Thomas Engbers is a researcher at the forefront of intelligent manufacturing and automated assembly, with a core focus on reducing the costly, time-intensive ramp-up phases that plague modern production lines. His major contribution lies in pioneering the use of artificial neural networks to autonomously determine optimal assembly parameters, directly addressing the planning uncertainties and financial risks associated with manual parameter variations. This work, detailed in his most-cited 2018 paper (12 citations), offers a transformative path toward self-optimizing assembly systems. Engbers also tackles the unique challenges of handling delicate, non-rigid materials, as demonstrated in his 2019 study on a fast pick-and-place stacking system for thin, limp fuel cell components (6 citations). By solving the complex handling issues of inhomogeneous parts, his research bridges the gap between advanced robotics and sustainable energy technologies. Engbers’ work is highly relevant for students and engineers seeking to understand how machine learning can streamline industrial automation, reduce waste, and accelerate the deployment of next-generation manufacturing systems.
Research Focus
Key Achievements
Top Papers
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