Physics–AI Dual-Driven Prediction of CNC Following-Up Errors and Compensation Control in High Precision Optics Machining
Year: 2026
Type: article
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.
DOI: 10.3390/jmmp10090332
Authors: Xin Chen, Kai Cheng, Yuanzheng Fu
Institutions: Shenyang University, Brunel University of London
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
Awards: G1273084962, G3953336734
OpenAlex ID: W7204993111