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Open AccessArticle

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

Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
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Symmetry 2019, 11(10), 1233; https://doi.org/10.3390/sym11101233
Received: 14 August 2019 / Revised: 10 September 2019 / Accepted: 20 September 2019 / Published: 2 October 2019
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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 with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent. View Full-Text
Keywords: tool wear state; CNN; BiLSTM; attention mechanism; signal features tool wear state; CNN; BiLSTM; attention mechanism; signal features
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MDPI and ACS Style

Chen, Q.; Xie, Q.; Yuan, Q.; Huang, H.; Li, Y. Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model. Symmetry 2019, 11, 1233.

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