Tool Wear Prediction Based on EMD–PSO–BiGRU Hybrid Model - Miaomiao Xin; Sze Song Ngu; Wanzhen Wang; Xiaomei Ni; Ting Wang; Pengpeng Wang; Yuxiang Zhang (Proceedings of Engineering and Technology Innovation, 2026)
To improve milling tool wear prediction accuracy, which is critical for intelligent manufacturing efficiency and cost reduction, a hybrid model based on empirical mode decomposition (EMD), particle swarm optimization (PSO), and bidirectional gated recurrent unit (BiGRU) is proposed. Raw machining signals are decomposed into intrinsic mode functions (IMFs) via EMD; the Pearson correlation coefficient (PCC) is then used to screen wear-related IMFs to eliminate redundancy. Subsequently, PSO is applied to optimize BiGRU parameters, hidden layer neurons, and learning rate, to reduce the risk of local optima. Validated on the PHM2010 dataset, the model increases R2 by 4.1%, reduces root mean square error (RMSE) by 13.9%, achieves a mean absolute percentage error (MAPE) of 5.915%, and outperforms improved subtraction-average-based optimizer (ISABO)-optimized BiGRU with 2.7% higher R2 and faster convergence. The contributions lie in the EMD–PCC screening strategy and the integrated hybrid model, providing a practical solution for industrial tool wear prediction.