Tool breakage monitoring driven by the real-time predicted spindle cutting torque using spindle servo signals

• Published in: Robotics and computer-integrated manufacturing 2025-04, Vol.92, p.102888, Article 102888
• Main Authors: Cheng, Yinghao, Li, Yingguang, Li, Guangxu, Liu, Xu, Xia, Jinyu, Liu, Changqing, Hao, Xiaozhong
• Format: Article
• Language: English
• Subjects: CNN; Cutting force prediction; LSTM; Spindle servo signals; Tool breakage monitoring
• ISSN: 0736-5845
• DOI: 10.1016/j.rcim.2024.102888

•A novel two-step data-driven tool breakage monitoring method using spindle servo signals is proposed.•The proposed method can accurately predict the spindle cutting torque for normal and broken tools.•The proposed method can achieve accurate and reliable tool breakage monitoring.•The proposed method has stable performance when increasing the detection frequency and decreasing training data. 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.