An adaptive GPR–EKF framework for uncertainty-calibrated cutting-force prediction

> 0.969. Uncertainty calibration is likewise improved: PICP exceeds 96%, average interval width is reduced by 24-25%, and interval variance is decreased by 49-60%. Ablation studies indicate that residual-gain scaling and mini-batch retraining contribute 1.8 and 2.4%-point reductions in NMAE, respectively, while EKF effectively suppresses short-term error spikes under high-frequency disturbances, highlighting enhanced robustness. Overall, the framework provides an interpretable, adaptive, and computationally feasible closed-loop solution for process monitoring and real-time adaptive compensation in intelligent manufacturing.