Life prediction of NC machine tools based on DT technology and LSTM technology

Jiwu Wang^*

• Qinhuangdao Polytechnic Institute, Qinhuangdao, China

Addressing the issues of physical model simplification errors, insufficient fusion of multi-source monitoring data, and low life prediction accuracy in traditional numerical control (NC) machine tool life prediction methods, this study proposes a remaining useful life (RUL) prediction method for NC machine tools that integrates digital twin (DT) technology with long short-term memory (LSTM) networks. The study constructs a multi-physics domain mapping model for NC machine tools based on DT technology, incorporates a multimodal data preprocessing module into the DT model to extract key degradation features of the machine tools, and develops an improved LSTM network. By inputting the high-dimensional degradation features output by the DT model into the LSTM network, precise RUL prediction of NC machine tools is achieved. To validate the efficacy of the introduced approach, experiments are conducted using a specific model of vertical machining center. The results demonstrate that the proposed model outperforms in all core metrics: achieving a prediction accuracy of 96.1%, an average absolute error of only 8.9 hours, and a maximum deviation of just 15 hours during the accelerated degradation phase, while maintaining a 100% physical constraint compliance rate and an efficient prediction speed of 22 milliseconds. Furthermore, as the system's proportion increases, the model's indicators rapidly improve; when the system proportion reaches 40%, the accuracy exceeds 40%, the recall rate approaches 42%, and the F0.5 score simultaneously increases significantly. These findings indicate that the proposed method can effectively reduce equipment downtime losses, enhance production efficiency, and provide a novel technological pathway for predictive maintenance of NC machine tools.