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

Abstract

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.

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“…Technologies such as deep learning and artificial intelligence have been applied to traditional manufacturing industries. Nonetheless, most of the current research focuses on data such as signal processing [2,3] and image detection [4,5]. However, the knowledge of the manufacturing industry often exists as unstructured textual data, such as manufacturing standards, a model transfer considering the active learning (MTAL) method is proposed.…”

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“…A study of the effect of medium viscosity on breakage parameters for wet grinding was presented in [7]. The monitoring method for the wear state of a tool using a convolutional bidirectional LSTM model was shown in the article [8]. The use of structural symmetries of a U12 engine using vibration analysis was presented in [9].…”