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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Data-based systematic error extraction and compensation methods based on wavelet transform in ultra-precision optical

Hanjie Li, Songlin Wan, Pandeng Jiang

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    A new data-based systematic error extraction and compensation (DSEC) method improves ultra-precision optics manufacturing. This technique enhances sub-aperture polishing accuracy by over 4 times, reducing surface figure errors significantly.

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    Area of Science:

    • Optics manufacturing
    • Precision engineering
    • Surface metrology

    Background:

    • Sub-aperture polishing is crucial for ultra-precision optics.
    • Existing methods struggle with complex polishing errors, impacting accuracy and efficiency.
    • Physical modeling limitations hinder compensation of certain systematic errors.

    Purpose of the Study:

    • To develop a data-based method for extracting and compensating systematic errors in sub-aperture polishing.
    • To enhance the accuracy and efficiency of optical component fabrication.
    • To improve the reliability of interferometer measurements by addressing piston term uncertainty.

    Main Methods:

    • Introduced wavelet transform for improved error extraction from small datasets.
    • Applied L1-norm optimization to reduce uncertainty in interferometer piston term measurements.
    • Verified the data-based systematic error extraction and compensation (DSEC) method using edge fluid loss and robot trajectory errors.

    Main Results:

    • Reduced surface figure root mean square (RMS) from 0.069λ to 0.017λ for an 85-mm mirror.
    • Achieved 0.019λ RMS for 610×440 mm mirrors after edge compensation, improving accuracy over 4 times.
    • Reached 0.011λ RMS for a 480×360 mm mirror after trajectory error compensation, improving accuracy over 2 times.

    Conclusions:

    • The proposed DSEC method effectively extracts and compensates systematic errors in sub-aperture polishing.
    • This data-driven approach significantly enhances polishing accuracy, exceeding 4x improvement in experiments.
    • The DSEC model provides valuable insights for advancing ultra-precision optics manufacturing.