Max Schneckenburger
Papers
6
Total Citations
38
H-Index
4
About
Max Schneckenburger is a researcher whose work spans two distinct but equally impactful domains: advanced manufacturing technologies and digital health innovation. He is best known for his sustained contributions to robot-assisted optical polishing, where he has pioneered the application of machine learning to predict and optimize material removal processes in the production of high-precision glass components such as astronomy mirrors, lithography lenses, and laser gyroscopes. Beginning with foundational work in 2019 and progressively refined through subsequent studies, his research addresses critical challenges in polishing stability, tool wear, and slurry erosion dynamics, earning a cumulative body of work that has attracted meaningful attention within the precision optics community. His most-cited paper (11 citations) represents a compelling departure into mobile health technology, investigating the accuracy of Apple's ARKit framework for measuring facial distances via smartphone — a study with significant implications for closing gaps in global healthcare access. Complementing this with vibration monitoring research in robotic polishing, Schneckenburger demonstrates a broad technical curiosity and a consistent commitment to bridging emerging technologies with real-world industrial and societal challenges.
Research Focus
Key Achievements
Top Papers
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- 4Machine learning robot polishing cell6 citations · 2019
- 5Machine learning model for robot polishing cell4 citations · 2020
- 6Monitoring and meaning of vibrations in robot polishing2 citations · 2023