Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror
Optical freeform surfaces are widely used in imaging and non-imaging systems due to their high design freedom. In freeform surface manufacturing and assembly, dynamic freeform surface measurement that can guide the next operation remains a challenge. To meet this urgent need, we propose a dynamic in...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/490 |
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| author | Xu Chang Yao Hu Jintao Wang Xiang Liu Qun Hao |
| author_facet | Xu Chang Yao Hu Jintao Wang Xiang Liu Qun Hao |
| author_sort | Xu Chang |
| collection | DOAJ |
| description | Optical freeform surfaces are widely used in imaging and non-imaging systems due to their high design freedom. In freeform surface manufacturing and assembly, dynamic freeform surface measurement that can guide the next operation remains a challenge. To meet this urgent need, we propose a dynamic interferometric method based on a machine learning-configured deformable mirror (DM). In this method, a dynamic interferometric system is developed. By using coaxial structure and polarization interference, transient measurement of the measured surface can be realized to meet dynamic requirements, and at the same time, DM transient monitoring can be realized to reduce the accuracy loss caused by DM surface changes and meet dynamic requirements. A transient phase modulation scheme using machine learning to configure the DM surface is proposed, which keeps the system in a measurable state. Compared with the traditional phase modulation scheme that relies on iteration, the scheme proposed in this paper is more efficient and is conducive to meeting dynamic requirements. The feasibility is verified by practical experiments. The research in this paper has significance for guiding the application of dynamic interferometry in the measurement of dynamic surfaces. |
| format | Article |
| id | doaj-art-13815d690a3c4779baba686f6ae09f94 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-13815d690a3c4779baba686f6ae09f942025-01-24T13:49:07ZengMDPI AGSensors1424-82202025-01-0125249010.3390/s25020490Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable MirrorXu Chang0Yao Hu1Jintao Wang2Xiang Liu3Qun Hao4Institute of Mechanics and Acoustics Metrology, National Institute of Metrology, Beijing 100029, ChinaBeijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaInstitute of Mechanics and Acoustics Metrology, National Institute of Metrology, Beijing 100029, ChinaInstitute of Mechanics and Acoustics Metrology, National Institute of Metrology, Beijing 100029, ChinaBeijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaOptical freeform surfaces are widely used in imaging and non-imaging systems due to their high design freedom. In freeform surface manufacturing and assembly, dynamic freeform surface measurement that can guide the next operation remains a challenge. To meet this urgent need, we propose a dynamic interferometric method based on a machine learning-configured deformable mirror (DM). In this method, a dynamic interferometric system is developed. By using coaxial structure and polarization interference, transient measurement of the measured surface can be realized to meet dynamic requirements, and at the same time, DM transient monitoring can be realized to reduce the accuracy loss caused by DM surface changes and meet dynamic requirements. A transient phase modulation scheme using machine learning to configure the DM surface is proposed, which keeps the system in a measurable state. Compared with the traditional phase modulation scheme that relies on iteration, the scheme proposed in this paper is more efficient and is conducive to meeting dynamic requirements. The feasibility is verified by practical experiments. The research in this paper has significance for guiding the application of dynamic interferometry in the measurement of dynamic surfaces.https://www.mdpi.com/1424-8220/25/2/490dynamic interferometryfreeform surfacemachine learning |
| spellingShingle | Xu Chang Yao Hu Jintao Wang Xiang Liu Qun Hao Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror Sensors dynamic interferometry freeform surface machine learning |
| title | Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror |
| title_full | Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror |
| title_fullStr | Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror |
| title_full_unstemmed | Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror |
| title_short | Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror |
| title_sort | dynamic interferometry for freeform surface measurement based on machine learning configured deformable mirror |
| topic | dynamic interferometry freeform surface machine learning |
| url | https://www.mdpi.com/1424-8220/25/2/490 |
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