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Deep Learning-Enabled Real Time In-Site Quality Inspection Based On Gesture Classification

  • In this paper we present a novel method for performing in site real time quality inspection (QI) and consequently, digitalization of manual processes performed by human workers. It complements and improves our previous work in this area, which makes use of telemetry gathered from a smartwatch to classify manual actions as successful or unsuccessful. This new methodology provides the worker with a real time capable, robust and more accurate quality inspector. This work enhances the existing system through the elimination of input from the user by making use of a BIOX bracelet that detects gestures. The signal processing and classification methods are simplified and optimised by using assembled neural networks thus merging together the data gathered from multiple signal sources. Consequently, the overall QI system is improved with around 70%, thus furthering the necessary development needed to have a system ready to be used on a production environment.

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Metadaten
Author: Ioan-Matei Sarivan, Stefan Andreas Baumann, Daniel Díez Álvarez, Felix Euteneuer, Matthias Reichenbach, Ulrich BergerORCiD, Ole Madsen, Simon Bøgh
DOI:https://doi.org/10.1007/978-3-662-62962-8_26
ISBN:978-3-662-62961-1
Title of the source (English):Advances in Automotive Production Technology – Theory and Application; Stuttgart Conference on Automotive Production (SCAP2020)
Publisher:Springer
Place of publication:Berlin
Editor: Philipp Weißgraeber, Frieder Heieck, Clemens Ackermann
Document Type:Part of a book (chapter)
Language:English
Year of publication:2021
First Page:221
Last Page:229
Series ; volume number:ARENA2036
Faculty/Chair:Fakultät 3 Maschinenbau, Elektro- und Energiesysteme / FG Automatisierungstechnik
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