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Updated: Aug 2, 2025

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
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On-axis deflectometric system for freeform surface measurement.

Shengtai Zhu, Daodang Wang, Wenjun Kang

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    |April 14, 2023
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new on-axis deflectometric system for precise freeform surface measurement. This system uses a miniature mirror and deep learning to accurately test surfaces with large slopes, offering a cost-effective solution.

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    Area of Science:

    • Optical Engineering
    • Metrology
    • Surface Metrology

    Background:

    • Accurate measurement of freeform surfaces is crucial in advanced manufacturing.
    • Existing deflectometric methods face challenges with large slope ranges and complex geometries.
    • On-axis testing configurations are desirable for simplifying optical paths.

    Purpose of the Study:

    • To propose and validate an on-axis deflectometric system for accurate freeform surface measurement.
    • To address the limitations of current methods in handling large slope ranges.
    • To enable flexible and general testing of freeform surfaces, including on-machine applications.

    Main Methods:

    • Implementation of an on-axis deflectometric system using a miniature folding mirror.
    • Integration of a deep-learning method to recover missing surface data from a single measurement.
    • Validation of system feasibility and accuracy through experimental testing.

    Main Results:

    • The proposed system achieves accurate measurement of freeform surfaces with large slope ranges.
    • Deep learning effectively recovers missing surface data caused by the folding mirror.
    • The system demonstrates low sensitivity to calibration errors and high testing accuracy.
    • Feasibility and accuracy have been experimentally validated.

    Conclusions:

    • The developed on-axis deflectometric system offers a feasible, low-cost, and simple solution for freeform surface testing.
    • The integration of deep learning enhances data recovery and measurement accuracy.
    • The system shows significant potential for on-machine testing applications.