A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

As a typical manufacturing equipment, CNC machine tool (CNCMT) is the mother machine of industry. Fault of CNCMT might cause the loss of precision and affect the production if troubleshooting is not timely. Therefore, the reliability of CNCMT has a big significance. Predictive maintenance is an effective method to avoid faults and casualties. Due to less consideration of the status variety and consistency of CNCMT in its life cycle, current methods cannot achieve accurate, timely and intelligent results. To realize reliable predictive maintenance of CNCMT, a hybrid approach driven by Digital Twin (DT) is studied. This approach is DT model-based and DT data-driven hybrid. With the proposed framework, a hybrid predictive maintenance algorithm based on DT model and DT data is researched. At last, a case study on cutting tool life prediction is conducted. The result shows that the proposed method is feasible and more accurate than single approach.