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High-precision autocollimation method based on a multiscale convolution neural network for angle measurement.

Jian Shi, Yuechao Li, Zixi Tao

    Optics Express
    |October 27, 2022
    PubMed
    Summary

    A novel multiscale convolution neural network (MSCNN) enhances autocollimation angle measurement precision. This method significantly reduces uncertainty and corrects nonlinear errors, improving accuracy sevenfold compared to traditional principles.

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    Area of Science:

    • Optical Engineering
    • Metrology
    • Artificial Intelligence in Measurement

    Background:

    • Autocollimation systems are crucial for high-precision angle measurements.
    • Traditional methods face limitations due to aberrations and assembly errors, leading to nonlinear errors and increased uncertainty.

    Purpose of the Study:

    • To propose a high-precision autocollimation method using a multiscale convolution neural network (MSCNN).
    • To integrate MSCNN with traditional measurement models to improve angle measurement accuracy and reduce uncertainty.
    • To address and correct nonlinear errors inherent in autocollimation systems.

    Main Methods:

    • Developed a measurement method integrating MSCNN with a traditional autocollimation model.
    • Leveraged MSCNN's multiscale representation learning to extract features from spot shape and gray distribution.

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  • Constructed an accurate nonlinear measurement model to account for aberrations and assembly errors.

    • Main Results:

    • Achieved an extended uncertainty of 0.29 arcsec (k=2) in angle measurements.
    • Demonstrated a sevenfold improvement in accuracy compared to traditional measurement principles.
    • Successfully resolved nonlinear errors caused by aberrations and assembly errors in the collimating optical path.

    Conclusions:

    • The proposed MSCNN-based autocollimation method significantly enhances angle measurement precision.
    • The method effectively mitigates nonlinear errors, offering a substantial improvement over conventional techniques.
    • The approach exhibits good universality and applicability to various autocollimation systems.