Bøgh, Simon

Díez Alvarez, Daniel

Euteneuer, Felix

Greiner, Johannes

Madsen, Ole

Reichenbach, Matthias

Sarivan, Ioan-Matei

English

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

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

https://vbn.aau.dk/files/431644731/1_s2.0_S2351978920319089_main.pdf

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

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