Abstract: To monitor the tool wear state of Computerized Numerical Control (CNC) machining equipment in real time in a manufacturing workshop,a lightweight real-time monitoring method based on deep gated recurrent unit neural network was proposed.The wavelet threshold denoising on the timing signals collected by the acceleration sensors was performed in the preprocessing stage,and the redundant signal generated by each tool feed was divided into a plurality of training samples to filter out noise and improve the robustness of the algorithm.Convolutional Neural Network (CNN) was used to extract deep features from time-series signal as an input,and then the deep Bidirectional Gated Recurrent Unit (BiGRU) neural network was constructed to learn the time-series information between the feature vectors.Attention mechanism was introduced to self-adaptively perceive the network weights associated with the classification results of wear state and distribute the weights reasonably.In this way,the limitations of traditional manual feature extraction steps could be avoided.The experimental results demonstrated that the proposed method could accurately predict the tool wear state in real time for raw data collected by the sensors,and achieve better results in recognition accuracy and generalization ability,which provided a new way to detect tool wear state in practical industrial scenarios.

Key words: tool wear state, real-time monitering, wavelet denoising, convolutional neural network, bidirectional gated recurrent unit, Attention mechanism

摘要： 为监测生产加工过程中的刀具磨损状态,提出一种基于深度门控循环单元神经网络的轻量化状态监测模型。首先,预处理阶段对加速度传感器采集的时序信号进行小波阈值去噪,并将每次刀具进给产生的冗长信号划分为多个训练样本,以滤除噪声、改善算法的鲁棒性;然后,利用卷积神经网络(CNN)从时序信号输入中自适应地提取特征,构建深度双向门控循环单元(BiGRU)神经网络学习特征向量间的时序信息,并将Attention机制的思想引入其中,自适应地感知对磨损状态分类结果有关联的网络权重,并对其进行合理分配,避免因人工提取特征带来的复杂性和局限性。实验结果表明,所提方法能够对传感器采集的原始数据实时准确地预测刀具磨损状态,在识别精度和泛化能力上均达到了较好的效果,为实际工业场景下的刀具磨损状态监测提供了新的思路。

关键词: 刀具磨损状态, 实时监测, 小波去噪, 卷积神经网络, 双向门控循环单元, Attention机制