关键词: 轮廓误差预测, 数字孪生模型, 多任务学习, 多时间步预测

Abstract: To improve the real-time prediction accuracy of contour errors in CNC machine tools,a mechanism-data hybrid-driven digital twin model was proposed.A mechanistic model integrating both the servo and mechanical systems was constructed to describe the dynamic response of the feed system under CNC interpolation commands.A time-series data-driven model integrated with multi-task learning was developed to compensate for the prediction residuals of the mechanistic model.In particular,to enhance the compensation capability of the data-driven model,a feature dataset integrating both simulation data and real-time data was constructed for model training.Finally,multiple sets of spatial motion trajectories were designed to conduct model validation experiments,during which the predictive performance of various models was systematically compared.The results indicated that the proposed hybrid twin model maintains high prediction accuracy across multiple time-step contour error prediction tasks.

Key words: contour error prediction, digital twin model, multi-task learning, multi-step prediction