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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Vydané v:	IEEE transactions on industrial electronics (1982) Ročník 66; číslo 5; s. 3794 - 3803
Hlavní autori:	Shi, Chengming, Panoutsos, George, Luo, Bo, Liu, Hongqi, Li, Bin, Lin, Xu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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