Automated gap and flush measurements between car parts assisted by a highly flexible and accurate robot system
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Tobias Schröder
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Volker Schwieger
Abstract
This research investigates the application of gap and flush measurements between structural car components as part of the quality assurance process in Mercedes-Benz production. The measuring tool, attached to a lightweight robot (LBR), is mounted on a driverless platform Kuka-Motion-Platform (KMP). The study collects measurement data during three critical production phases: car body construction, car varnishing, and body assembly. Throughout the entire production process, observing gap and flush values ensures the fulfilment of both functional and customer requirements. At the assembly line, two measurement approaches are used: Full Automatic Gap-Measuring Installations (SMA): These installations automatically measure gap and flush values. If errors are detected during measurement, the affected car bodies are diverted from the production lane. Hand-Held Instruments: Adjacent to the production line, two employees perform rework and repeat quality measurements to meet the required quality targets. Once corrections are successfully executed, the cars re-enter the production line. The study explores gap measurement using mobile measurement units (SME) in human-robot interaction (HRC). These mobile units, currently fixed in position, offer enhanced automation and flexibility within the production process. Future plans involve replacing the static SMA with systems like SME. Additionally, the research aims to develop a scalable and autonomous measuring system. Toward this goal, two main components from the SME the measurement devices and the LBR, are mounted on the KMP. The empirical determination of measurement device variance at the desired gap is achieved through propagation of uncertainty. In summary, this study contributes to the advancement of quality assurance practices in Mercedes-Benz car production by leveraging innovative measurement techniques and automation.
Acknowledgments
I would like to thank the following people for the breathtaking support in this research project. Special thanks go to Matthias Hornung, as my second supervisor and mentor at the Mercedes-Benz plant. He left nothing unmentioned or undone to support me in the best way possible. I also owe a debt of gratitude for the massive support by my students Roman Buss, Matthias Rupp and Lukas Schröder. Thanks!
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The main author (Mr. Schröder) is employee of Mercedes-Benz Tech innovation. All other authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Patents: See Citation [26] (DE 10 2018 008 209 A1) and [17] (DE 10 2020 006 160 A1).
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