An Ultra-Precision Smoothing Polishing Model for Optical Surface Fabrication with Morphology Gradient Awareness.

Area of Science:

• Optical engineering
• Surface metrology
• Manufacturing science

Background:

• Ultra-precision optical components require high surface quality, with mid-spatial frequency (MSF) errors being a significant challenge.
• Existing methods for surface error suppression often lack dynamic adaptability to complex surface topographies.

Purpose of the Study:

• To develop and validate a novel morphology gradient-aware spatiotemporal coupled smoothing model for enhancing surface morphology quality.
• To specifically address and suppress mid-spatial frequency (MSF) errors in ultra-precision optical components.

Main Methods:

• A convolutional material removal model incorporating a Laplacian curvature-sensitive "peak-priority" mechanism was developed.
• A nonlinear spatiotemporal coupling equation adaptively modulated dwell time based on surface gradient, curvature, and periodic fluctuations.
• Material removal was modeled via convolution of a removal function with a dynamic dwell time distribution.

Main Results:

• The proposed model demonstrated significant improvements in surface smoothing performance.
• Effective suppression of mid-spatial frequency (MSF) errors was achieved through dynamic dwell time control.
• Analytical expressions for performance metrics (PV, RMS, PSD) enabled quantitative assessment.

Conclusions:

• The morphology gradient-aware spatiotemporal coupled smoothing model offers a robust solution for improving surface quality in ultra-precision optics.
• The "peak-priority" mechanism and adaptive dwell time control are key to enhanced MSF error suppression.
• The model provides a quantitative framework for assessing and optimizing the smoothing process.