Application of Deep Learning Algorithms to the Study of the Relationship between Acoustic Emission Signals and Grinding Force Parameters

The article considers prediction of cutting force components based on analysis of acoustic emission (AE) signals using deep learning algorithms. Based on pre-processing and synchronization of experimental data obtained during grinding of a heat-resistant nickel alloy, a training sample based on spec...

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Veröffentlicht in:	Russian engineering research Jg. 45; H. 6; S. 765 - 770
Hauptverfasser:	Mitrofanov, A. P., Rastegaev, I. A., Novikov, A. V.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.06.2025 Springer Nature B.V
Schlagworte:	Accuracy Acoustic emission Acoustics Algorithms Cutting force Data compression Deep learning Engineering Engineering Design Experiments Grinding Lubricants & lubrication Machine learning Neural networks Prediction models Process controls Sensors Software Spectrograms Synchronism United States > US Switzerland deep learning spectrograms acoustic emission cutting force grinding ResNet
ISSN:	1068-798X, 1934-8088
Online-Zugang:	Volltext
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