Research on Tool Condition Monitoring and Health Management of CNC Machine Tools Based on Digital Twin

Aiming at the problems of low monitoring accuracy of tool condition and insufficient intelligence of health management, takes the CNC machining center milling titanium alloyas the research object and proposes a tool life cycle health management method based on Digital Twin. By constructing a four-layer digital twin architecture, the bidirectional real-time interaction between the physical machine tool and the virtual model is realized. Through the fusion of multi-source sensor data, a hybrid prediction model based on MSCNN-BiLSTM network is developed, combined with cross-scale dual attention mechanism, which effectively improves the accuracy of tool wear state recognition. The experimental results show that the proposed method can achieve a wear state recognition accuracy of 99.72% in titanium alloy milling, with a root mean square error and mean absolute error of the remaining life of 0.9537 and 0.7860, respectively. Compared with the traditional method, the prediction accuracy is improved. At the same time, the digital twin system developed based on Unity3D realizes the visualization monitoring and intelligent early warning function of tool condition. The results show that the system improves the overall equipment efficiency by 15% to 20%, providing an effective solution for tool health management in the intelligent manufacturing environment.