Mr. Zheng Lv Profile

Zheng Lv

at Xi'an Univ. of Posts

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Proceedings Article | 20 January 2023 Paper

Design of dispersive objective lens for spectral confocal displacement sensor based on GRIN lens

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

Proceedings Volume 12558, 125580A (2023) https://doi.org/10.1117/12.2651399

KEYWORDS: GRIN lenses, Confocal microscopy, Objectives, Sensors, Refractive index, Lens design, Zemax, Precision measurement, Light, Gradient-index optics

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Spectral confocal technology uses chromatic aberration which is generated by a dispersion lens to detect surface shape. The axial dispersion generated by the dispersion lens will affect the measurement range of the whole spectral confocal displacement sensor. The refractive index of an axial GRIN (gradient index, GRIN) lens varies non-homogeneous along the axial direction and is constantly perpendicular to an optical axis of the plane. The paper explored the design of a dispersive objective lens for a spectral confocal displacement sensor based on the GRIN lens. Firstly, the optical power and axial dispersion models of the GRIN lens are established. The axial dispersion can be realized by focusing the light of different wavelengths at different positions of the optical axis. Secondly, based on the optic power and dispersion function of the GRIN lens, the refractive index distribution of the GRIN lens and the simulation design of the dispersive objective lens is obtained by using MATLAB and ZEMAX software respectively. Finally, the GRIN dispersive objective lens is optimized by setting different merit function operands. The experimental results show that the axial GRIN lens can achieve a focal shift of 1130μm within the wavelength range from 486nm to 656nm. Moreover, the linearity of the lens behaves well. All the blur spot is much smaller than the airy spot. The lens has well-focused as well as high-precision. The research results provide a reference and theoretical basis for the application of the GRIN lens in spectral confocal technology.

Proceedings Article | 20 January 2023 Paper

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, 1256309 (2023) https://doi.org/10.1117/12.2652096

KEYWORDS: Neural networks, Machine learning, Signal processing, Sensors, Networks, Light sources, Light, Calibration, Genetic algorithms

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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.

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