Physics–AI Dual-Driven Prediction of CNC Following-Up Errors and Compensation Control in High Precision Optics Machining

Abstract

Optical surface deviations in ophthalmic optics arise from fast tool servo errors, the tool footprint, freeform surface residuals, and the freeform surface curvature responses across processing stages. In this paper, a PAM-Net physics–AI dual-driven compensation control method is presented for addressing the issues in dynamically accurate positioning of a diamond cutting tool via the fast tool servo using existing methods, e.g., struggling to characterize micrometer-scale CNC following-up errors, spatial surface-form perturbations, and S/C optical quality simultaneously. PAM-Net maps Z-axis position, velocity, acceleration, jerk, and A/B-axis phases to surface-form residuals and S/C deviations through tool-lens projection and curvature-mediated optical-response operators, while jointly estimating uncertainty and safety risk for constrained NC compensation. The framework also preserves an interpretable mediation chain from servo dynamics to final optical quality. On holdout-35, removing acceleration/jerk increased RMSE from 0.512 to 5.395 μm, indicating strong predictive dependence on high-order servo dynamics. Closed-loop validation increased the strict ±0.12 D pass rate from 72.5% to 87.5%, alongside reduced surface-form and curvature residuals. These results indicate that learning-based compensation control for high-precision freeform-optics manufacturing requires joint consideration of prediction accuracy, physical interpretability, executable NC write-back, and manufacturing constraints.