Monitoring tool breakage during computer numerical control machining is essential to ensure machining quality and equipment safety. In consideration of the low cost in long-term use and the non-invasiveness to workspace, using servo signals of machine tools to monitor tool breakage has been viewed as the solution that has great potential to be applied in real industry. However, because machine tool servo signals can only partially and indirectly reflect tool conditions, the accuracy and reliability of existing methods still need to be improved. To overcome this challenge, a novel two-step data-driven tool breakage monitoring method using spindle servo signals is proposed. Since spindle cutting torque is acknowledged as one of the most effective and reliable physical signals for detecting tool breakage, it is introduced as the key intermediate variable from spindle servo signals to tool conditions. The monitored spindle servo signals are used to predict the spindle cutting torque in real time based on a long short-term memory neural network, and then the predicted spindle cutting torque is used to detect tool breakage based on a one-dimensional convolutional neural network. The experimental results show that the proposed method can accurately predict the spindle cutting torque for normal tools and broken tools. Compared with the tool breakage monitoring methods that directly use spindle servo signals, the proposed method has higher detection accuracy and more reliable detection results, and the performance is more stable when increasing the detection frequency and decreasing training data.

中文翻译：

---

使用主轴伺服信号，由实时预测的主轴切削扭矩驱动刀具破损监控


在计算机数控加工过程中监控刀具破损对于确保加工质量和设备安全至关重要。考虑到长期使用成本低和对工作空间的非侵入性，使用机床的伺服信号来监测刀具破损已被视为在实际工业中具有巨大应用潜力的解决方案。然而，由于机床伺服信号只能部分和间接地反映刀具状态，因此现有方法的精度和可靠性仍有待提高。为了克服这一挑战，提出了一种使用主轴伺服信号的新型两步数据驱动刀具破损监测方法。由于主轴切削扭矩被认为是检测刀具破损的最有效和最可靠的物理信号之一，因此它被引入作为从主轴伺服信号到刀具状态的关键中间变量。利用监测到的主轴伺服信号，基于长短期记忆神经网络实时预测主轴切削扭矩，然后基于一维卷积神经网络，利用预测的主轴切削扭矩检测刀具破损。实验结果表明，所提方法能够准确预测普通刀具和破损刀具的主轴切削扭矩。与直接使用主轴伺服信号的刀具破损监测方法相比，所提方法具有更高的检测精度和更可靠的检测结果，并且在增加检测频率和减少训练数据时性能更加稳定。