Prediction of thin-walled workpiece machining error: a transfer learning approach

The surface error induced by low-rigid deformation and intermittent cutting is common in the milling process of thin-walled workpieces. Machining errors have a direct impact on the surface accuracy of the machined workpiece, making it crucial to monitor the milling error throughout the thin-walled workpiece machining process. This article provides a strategy for forecasting machining errors in thin-walled workpieces. The prediction strategy faces two difficulties: the flexibility variations in the different machining positions of the thin-walled workpieces and the processing information shifting with the varied machining conditions. To tackle these challenges, the knowledge-embedded parameter construction of the strategy establishes a correlation between error and process information by integrating physical constraints and data information. Transfer learning combines a small amount of real-time data with a large amount of historical data, enabling effective practical data application and reutilization. The experimental evaluations and comparisons have demonstrated the predictive performance and applicability of the machining error prediction strategy.