Intelligent Contour Error Compensation of Ultraprecision Machining Using Hybrid Mechanism-Data-Driven Model Assisted With IoT Framework

To address the complicated modeling process and inadequate explainability of current methods for improving the contour accuracy of ultraprecision machining (UPM), this study presented an Internet of Things (IoT)-based contour error compensation (CEC) framework. To achieve a convincing and real-time compensation solution, a hybrid mechanism-data-driven CEC model was created that integrated the 1DCNN-BiLSTM-attention model for predicting the axis actual positions, contour error estimation, and bidirectional compensation algorithms. Bayesian hyperparameter optimization and sensitivity analysis were used in the proposed models to improve the prediction accuracy of the actual position of each axis, with high-quality training datasets from well-designed experiments. Finally, validating the system on a three-axis ultraprecision milling machine demonstrated its superior performance. This study first demonstrated the feasibility of a deep learning approach for improving UPM accuracy, which will assist in accelerating digitalization and intellectualization for UPM.