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Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror.

Xu Chang¹, Yao Hu², Jintao Wang¹

¹Institute of Mechanics and Acoustics Metrology, National Institute of Metrology, Beijing 100029, China.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

This study introduces a dynamic interferometric method using a machine learning-configured deformable mirror (DM) for real-time measurement of optical freeform surfaces. This approach enhances efficiency and accuracy in dynamic surface metrology.

Keywords:

dynamic interferometry freeform surface machine learning

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Area of Science:

Optical Engineering
Metrology
Machine Learning Applications

Background:

Optical freeform surfaces offer high design freedom but pose challenges in dynamic measurement for manufacturing and assembly.
Real-time feedback during freeform surface fabrication is crucial for guiding subsequent operations.

Purpose of the Study:

To develop a dynamic interferometric measurement method for freeform surfaces.
To address the challenge of real-time metrology in freeform surface manufacturing and assembly.

Main Methods:

A dynamic interferometric system employing a coaxial structure and polarization interference was developed.
A machine learning-configured deformable mirror (DM) was utilized for transient phase modulation.
The system incorporates transient monitoring of the DM to mitigate accuracy loss due to surface changes.

Main Results:

The proposed method enables transient measurement of freeform surfaces, meeting dynamic requirements.
Machine learning-based DM configuration proved more efficient than traditional iterative methods.
Experimental verification confirmed the feasibility and effectiveness of the dynamic interferometric approach.

Conclusions:

The developed dynamic interferometric method offers an efficient solution for measuring dynamic freeform surfaces.
This research provides a foundation for applying dynamic interferometry in advanced optical manufacturing.
The use of machine learning for DM configuration significantly improves measurement efficiency and adaptability.