Enabling Real-Time Quality Inspection in Smart Manufacturing Through Wearable Smart Devices and Deep Learning

Ioan-Matei Sarivan*, Johannes Greiner, Daniel Díez Alvarez, Felix Euteneuer, Matthias Reichenbach, Ole Madsen, Simon Bøgh

*Kontaktforfatter

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Abstrakt

In this paper, we present a novel method for utilising wearable devices with Convolutional Neural Networks (CNN) trained on acoustic and accelerometer signals in smart manufacturing environments in order to provide real-time quality inspection during manual operations. We show through our framework how recorded or streamed sound and accelerometer data gathered from a wrist-attached device can classify certain user actions as successful or unsuccessful. The classification is designed with a Deep CNN model trained on Mel-frequency Cepstral Coefficients (MFCC) from the acoustic input signals. The wearable device provides feedback on three different modalities: audio, visual and haptic; thus ensuring the worker’s awareness at all time. We validate our findings through deployments of the complete AI-enabled device in production facilities of Mercedes-Benz AG. From the conducted experiments it is concluded that the use of acoustic and accelerometer data is valuable to train a classifier with the purpose of action examination during industrial assembly operations, and provides an intuitive interface for ensuring continued and improved quality inspection.
OriginalsprogEngelsk
TidsskriftProcedia Manufacturing
ISSN2351-9789
StatusAccepteret/In press - 2020
Begivenhed30th International Conference on Flexible Automation and Intelligent Manufacturing - Athens, Grækenland
Varighed: 15 jun. 202118 jun. 2021
https://www.faimconference.org/

Konference

Konference30th International Conference on Flexible Automation and Intelligent Manufacturing
LandGrækenland
ByAthens
Periode15/06/202118/06/2021
Internetadresse

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