JIANG Yi-feng, HU Sheng, LIU Wen-hui, ZHANG Qing, YANG Jin-xi
The machining quality of electric spindles critically determines precision, efficiency, and stability in precision manufacturing. However, the machining process faces challenges due to diverse product types, multiple operating conditions and scarce target-condition data, making consistent quality of electric spindle difficult to guarantee. To address this, this paper proposes a transfer-learning-based method for multi-operating-condition quality prediction. The method first extracts spindle time-series signals and employs the Synthetic Minority Over-sampling Technique to balance historical and target-condition data distributions. Subsequently, constructs a two-stage regression model, TrAdaboost.R2, and leverages knowledge transfer to predict spindle quality under target conditions. Finally, the proposed method is validated with electric spindle data, demonstrating its superior prediction performance. This approach provides an effective framework for the precise quality prediction of electric spindles across varying operating conditions.