Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels

Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels

Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grin...

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Bibliographic Details
Main Authors: D. González, J. Alvarez, J. A. Sánchez, L. Godino, I. Pombo
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Sensors
Subjects:
deep learning
acoustic emission
grinding
feature extraction
Online Access:https://www.mdpi.com/1424-8220/22/18/6911

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