Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network...

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Bibliographic Details
Main Authors: Qipeng Chen, Qingsheng Xie, Qingni Yuan, Haisong Huang, Yiting Li
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Symmetry
Subjects:
tool wear state
cnn
bilstm
attention mechanism
signal features
Online Access:https://www.mdpi.com/2073-8994/11/10/1233

Internet

https://www.mdpi.com/2073-8994/11/10/1233

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