Machining quality prediction using acoustic sensors and machine learning

Carrino, Stefano (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Guerne, Jonathan (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Dreyer, Jonathan (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Ghorbel, Hatem (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Schorderet, Alain (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Montavon, Raphaël (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland)

The online automatic estimation of the quality of products manufactured in any machining process without any manual intervention represents an important step toward a more efficient, smarter manufacturing industry. Machine learning and Convolutional Neural Networks (CNN), in particular, were used in this study for the monitoring and prediction of the machining quality conditions in a high-speed milling of stainless steel (AISI 303) using a 3mm tungsten carbide. The quality was predicted using the Acoustic Emission (AE) signals captured during the cutting operations. The spectrograms created from the AE signals were provided to the CNN for a 3-class quality level. A promising average f1-score of 94% was achieved.


Keywords:
Conference Type:
published full paper
Faculty:
Ingénierie et Architecture
School:
HE-Arc Ingénierie
HEIG-VD
Institute:
COMATEC - Institut de Conception, Matériaux, Emballage & Conditionnement
Publisher:
Mures, Romania, 8-9 October 2020
Date:
2020-10
Mures, Romania
8-9 October 2020
Pagination:
10p.
Published in:
Proceedings of the 14th International Conference INTER-ENG 2020 Interdisciplinarity in Engineering, 8–9 October 2020, Mures, Romania
DOI:
ISSN:
2504-3900
Appears in Collection:



 Record created 2021-01-05, last modified 2021-01-07

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