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

Yu-Yue Yu, Da-Ming Shi, Han Ding and Xiao-Ming Zhang ()
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Yu-Yue Yu: Huazhong University of Science and Technology
Da-Ming Shi: Huazhong University of Science and Technology
Han Ding: Huazhong University of Science and Technology
Xiao-Ming Zhang: Huazhong University of Science and Technology

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 4, No 29, 2803-2827

Abstract: Abstract 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.

Keywords: Machining error prediction; Thin-walled workpiece; Transfer learning; Incremental learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02382-7

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