Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing

Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning has been successfully applied to numerous classification tasks in manufacturing, often to forecast pa...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) Jg. 66; H. 5; S. 3794 - 3803
Hauptverfasser:	Shi, Chengming, Panoutsos, George, Luo, Bo, Liu, Hongqi, Li, Bin, Lin, Xu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification Computer simulation Deep learning Deep learning (DL) Feature extraction feature fusion feature spaces Hidden Markov models Machine learning Machinery condition monitoring Machining Manufacturing Manufacturing processes tool condition monitoring (TCM) Tool wear ultraprecision manufacturing process
ISSN:	0278-0046, 1557-9948
Online-Zugang:	Volltext
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