Abstract
Despite the ongoing automation of modern production processes manual labor continues to be necessary due to its flexibility and ease of deployment. Automated processes assure quality and traceability, yet manual labor introduces gaps into the quality assurance process. This is not only undesirable but even intolerable in many cases.
We introduce a process monitoring system that uses inertial, magnetic field and audio sensors that we attach as add-ons to hand-held tools. The sensor data is analyzed via embedded classification algorithms and our system directly provides feedback to workers during the execution of work processes. We outline the special requirements caused by vastly different tools and show how to automatically train and deploy new ML models.
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Acknowledgements
This work was supported by the Bavarian Ministry of Economic Affairs, Infrastructure, Energy and Technology as part of the Bavarian project Leistungszentrum Elektroniksysteme (LZE) and through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II”.
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Löffler, C. et al. (2021). Automated Quality Assurance for Hand-Held Tools via Embedded Classification and AutoML. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_33
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DOI: https://doi.org/10.1007/978-3-030-67670-4_33
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