Due to issues such as limited variability in monitoring data across different tool wear conditions and interference during the machining process, data-driven monitoring models are susceptible to misclassification. Therefore, this paper proposes a pioneering approach that takes into account the tool wear law and the characteristic distribution of tool wear monitoring data. Specifically, the paper proposes an automatic feature extraction and tool condition monitoring method based on Siamese Long Short-term Memory Networks (SLSTMs) to transform the original data distribution, enhance the differentiation of different tool wear condition monitoring data, and achieve accurate prediction of tool wear condition. Additionally, a hybrid data and mechanism-driven tool wear monitoring method is proposed that takes advantage of the fact that tool wear is continuous and irreversible to enable reliable adjustment of the monitoring results of the data-driven model without human intervention. The study conducted milling experiments on a three-axis vertical machining center, using TA2 titanium alloy as the workpiece material. Vibration signals from the spindle in three directions were collected as input to the network, with tool wear state labeled as “0″ or ”1″ as the output. Classical machine learning algorithms such as Decision Trees, K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), as well as classical deep learning algorithms such as Convolutional Neural Networks (CNN), Sparse Stacked Autoencoders (SSAE) and Long Short-Term Memory Neural Networks (LSTM), were used to construct and compare feature extraction and state monitoring models. The proposed model based on Siamese Long Short-Term Memory Neural Networks (SLSTMs) achieved testing average accuracy of 98.2% (σ² = 0.44), outperforming typical deep learning algorithms such as CNN, LSTM and SSAE as well as traditional machine learning algorithms such as Decision Trees, KNN and SVM in terms of accuracy and robustness. Moreover, the proposed data and mechanism hybrid-driven tool wear monitoring method can effectively improve the monitoring accuracy and robustness of data-driven models such as CNN and SSAE. The monitoring accuracy of CNN and SSAE increased from 95.9% to 97.6% and 91.0% to 94.3%, respectively.

由于不同刀具磨损条件下监测数据的可变性有限以及加工过程中的干扰等问题，数据驱动的监测模型容易出现错误分类。因此，本文提出了一种兼顾刀​​具磨损规律和刀具磨损监测数据特征分布的开创性方法。具体来说，本文提出了一种基于孪生长短期记忆网络（SLSTMs）的自动特征提取和刀具状态监测方法，对原始数据分布进行变换，增强不同刀具磨损状态监测数据的差异化，实现刀具的准确预测。磨损情况。此外，提出了一种混合数据和机制驱动的刀具磨损监测方法，该方法利用刀具磨损是连续且不可逆的事实，能够在无需人为干预的情况下可靠地调整数据驱动模型的监测结果。该研究在三轴立式加工中心上进行了铣削实验，采用TA2钛合金作为工件材料。来自主轴三个方向的振动信号被收集作为网络的输入，工具磨损状态标记为“0”或“1”作为输出。经典的机器学习算法，如决策树、K-最近邻（KNN）和支持向量机（SVM），以及经典的深度学习算法，如卷积神经网络（CNN）、稀疏堆叠自编码器（SSAE）和长短-术语记忆神经网络 (LSTM)，用于构建和比较特征提取和状态监测模型。基于孪生长短期记忆神经网络 (SLSTMs) 的拟议模型实现了 98.2% (σ²  = 0.44)，在准确性和鲁棒性方面优于典型的深度学习算法，如 CNN、LSTM 和 SSAE，以及传统的机器学习算法，如决策树、KNN 和 SVM。此外，所提出的数据和机制混合驱动的刀具磨损监测方法可以有效提高CNN和SSAE等数据驱动模型的监测精度和鲁棒性。CNN和SSAE的监测准确率分别从95.9%提高到97.6%，从91.0%提高到94.3%。