Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion

Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse a...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 16; H. 8; S. 5150 - 5159
Hauptverfasser:	Shi, Chengming, Luo, Bo, He, Songping, Li, Kai, Liu, Hongqi, Li, Bin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Condition monitoring Deep learning Feature extraction feature fusion Force Ground penetrating radar Informatics Machine learning Modelling Prediction models Predictive models stacked sparse autoencoders Tool wear tool wear prediction Vibrations
ISSN:	1551-3203, 1941-0050
Online-Zugang:	Volltext
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