Machining Quality Prediction Using Acoustic Sensors and Machine Learning

• Published in: Proceedings Vol. 63; no. 1; p. 31
• Main Authors: Stefano Carrino, Jonathan Guerne, Jonathan Dreyer, Hatem Ghorbel, Alain Schorderet, Raphael Montavon
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-12-2020
• Subjects: acoustic emission; condition monitoring; machine learning; micro-manufacturing; milling machines; quality prediction
• ISSN: 2504-3900
• DOI: 10.3390/proceedings2020063031

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 3 mm 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.