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Automated Quality Assurance for Hand-Held Tools via Embedded Classification and AutoML

 
: Löffler, C.; Nickel, C.; Sobel, C.; Dzibela, D.; Braat, J.; Gruhler, B.; Woller, P.; Witt, N.; Mutschler, C.

:

Dong, Y.:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. Proceedings. Pt.V : European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020
Cham: Springer Nature, 2021 (Lecture Notes in Artificial Intelligence 12461)
ISBN: 978-3-030-67669-8 (Print)
ISBN: 978-3-030-67670-4 (Online)
ISBN: 978-3-030-67671-1
S.532-535
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2020, Online>
Englisch
Konferenzbeitrag
Fraunhofer IIS ()

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.

: http://publica.fraunhofer.de/dokumente/N-637758.html