Research on spectral confocal displacement measurement method based on machine learning

Chunyan Li; Zheng Lv; Jihong Liu; Ke Li; Gengpeng Li; Dou Luo

doi:10.1117/12.2652096

20 January 2023 Research on spectral confocal displacement measurement method based on machine learning

Chunyan Li, Zheng Lv, Jihong Liu, Ke Li, Gengpeng Li, Dou Luo

Proceedings Volume 12563, AOPC 2022: AI in Optics and Photonics; 1256309 (2023) https://doi.org/10.1117/12.2652096
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

The chromatic confocal technology (CCT) uses the dispersion principle to establish an accurate encoding relationship between the spatial position and the axial focus point of each wavelength to achieve non-contact measurement. The accuracy of the measurement results is affected by the peak wavelength extraction accuracy. The flexible and adaptable characteristics of machine learning techniques are used to model the spectral wavelength and light intensity nonlinearly, establish the response relationship between input wavelength and output normalized light intensity, and refit the spectral curve distribution. In this paper, we apply the network of regression aspect of machine learning, Extreme Learning Machine (ELM), Back Propagation Neural Network (BPNN), and Genetic Algorithm optimized Back Propagation Neural Network(GA-BPNN) to fit the spectral response of the system to accurately locate the peak wavelength and compare it with the traditional peak extraction methods of Gaussian fitting, polynomial fitting, and center of the mass method to verify that the machine learning method used is significantly better than the traditional peak extraction methods in terms of peak extraction accuracy. The ELM network is the best among the three networks, with a peak extraction error of only 0.04μm and a Root Mean Square Error(RMSE) of only 6.8×10^-4. The analysis of calibration experiments, resolution, and stability experiments found that the ELM algorithm was found to have the shortest calculation time, and the system measurement resolution was explored through the ELM algorithm to be about 2μm. The research results of this paper have contributed to the improvement of the system measurement accuracy and measurement efficiency.

Citation Download Citation

Chunyan Li, Zheng Lv, Jihong Liu, Ke Li, Gengpeng Li, and Dou Luo "Research on spectral confocal displacement measurement method based on machine learning", Proc. SPIE 12563, AOPC 2022: AI in Optics and Photonics, 1256309 (20 January 2023); https://doi.org/10.1117/12.2652096

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KEYWORDS

Neural networks

Machine learning

Signal processing

Light sources

Networks

Sensors

Calibration

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