- 必威体育下载

姓名
邮箱
手机号码
标题
留言内容
验证码

Abstract

Laser is widely used in various fields such as laser processing, optical imaging, and optical trapping due to its high monochromaticity, directionality, and high energy density. However, the beam generated by the laser is a Gaussian beam with non-uniform distribution of optical energy, and this non-uniform distribution affects the interaction between the laser and the matter. Therefore, it is necessary to reshape the Gaussian beam into homogenized light spots with uniform distribution of optical energy. Laser beam homogenization method aims to change the spatial distribution of the Gaussian beam, precisely controlling the shape and intensity of the laser beam to achieve homogenized light spots. However, the existing laser beam homogenization methods encounter some problems such as complicated component preparation and poor flexibility. They also fail to address experimental errors caused by stray light and zero-order light interference, leading to discrepancies between the experimental results and the expected results. These limitations seriously restrict the widespread application of laser technology in various fields. A laser homogenization method based on machine learning is proposed for spatial light modulator (SLM) laser homogenization in this work. The preliminary approach to laser homogenization is to generate a phase hologram by using the Gerchberg-Saxton (G-S) algorithm and modulate the incident light beam into homogenized light spots by using an SLM. However, the inherent homogenization error of the SLM prevents laser homogenization from improving uniformity. The machine learning method is proposed as a means of compensating for homogenization errors, thereby improving the uniformity of the light spot. The corresponding supervised learning regression task on the experimental dataset establishes mapping relationships between the homogenization target images and the experimental detection images. The results of homogenization error compensation are validated through experiments. Compared with the traditional SLM laser homogenization methods, the proposed method reduces the non-uniformity of the light spot by 13%. The laser homogenization method based on machine learning is an efficient way to achieve laser beam homogenization. The proposed laser beam homogenization method can serve as a reference for machine learning-based method. This method possesses significant technical value for laser applications such as laser processing, optical imaging, and optical manipulation. Furthermore, it can provide guidance and reference for utilizing artificial intelligence in addressing optical problems.

Keywords:

Authors and contacts

Corresponding author:Liang Sheng,shliang@bjtu.edu.cn

Funds:Project supported by the National Natural Science Foundation of China (Grant No. 62375013).

FullText HTML: translate this paragraph

References

[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]

Cited By

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

DownLoad: Full-Size Img PowerPoint

[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]

[1]	Song Rui, Liu Xue-Mei, Wang Hai-Bin, Lü Hao, Song Xiao-Yan.Hardness prediction of WC-Co cemented carbide based on machine learning model. Acta Physica Sinica, 2024, 73(12): 126201.doi:10.7498/aps.73.20240284
[2]	Zhang Jia-Hui.Machine learning forin silicoprotein research. Acta Physica Sinica, 2024, 73(6): 069301.doi:10.7498/aps.73.20231618
[3]	Zhang Yi-Fan, Ren Wei, Wang Wei-Li, Ding Shu-Jian, Li Nan, Chang Liang, Zhou Qian.Machine learning combined with solid solution strengthening model for predicting hardness of high entropy alloys. Acta Physica Sinica, 2023, 72(18): 180701.doi:10.7498/aps.72.20230646
[4]	Guo Wei-Chen, Ai Bao-Quan, He Liang.Reveal flocking phase transition of self-propelled active particles by machine learning regression uncertainty. Acta Physica Sinica, 2023, 72(20): 200701.doi:10.7498/aps.72.20230896
[5]	Liu Ye, Niu He-Ran, Li Bing-Bing, Ma Xin-Hua, Cui Shu-Wang.Application of machine learning in cosmic ray particle identification. Acta Physica Sinica, 2023, 72(14): 140202.doi:10.7498/aps.72.20230334
[6]	Guan Xing-Yue, Huang Heng-Yan, Peng Hua-Qi, Liu Yan-Hang, Li Wen-Fei, Wang Wei.Machine learning in molecular simulations of biomolecules. Acta Physica Sinica, 2023, 72(24): 248708.doi:10.7498/aps.72.20231624
[7]	Yang Zhang-Zhang, Liu Li, Wan Zhi-Tao, Fu Jia, Fan Qun-Chao, Xie Feng, Zhang Yi, Ma Jie.Combining machine learning algorithm to improve prediction performance of ab initio method for vibrational energy spectra of HF/HBr/H³⁵Cl/Na³⁵Cl. Acta Physica Sinica, 2023, 72(7): 073101.doi:10.7498/aps.72.20221953
[8]	Ai Fei, Liu Zhi-Bing, Zhang Yuan-Tao.Numerical study of discharge characteristics of atmospheric dielectric barrier discharges by integrating machine learning. Acta Physica Sinica, 2022, 71(24): 245201.doi:10.7498/aps.71.20221555
[9]	Kang Jun-Feng, Feng Song-Jiang, Zou Qian, Li Yan-Jie, Ding Rui-Qiang, Zhong Quan-Jia.Machine learning based method of correcting nonlinear local Lyapunov vectors ensemble forecasting. Acta Physica Sinica, 2022, 71(8): 080503.doi:10.7498/aps.71.20212260
[10]	Zhang Jia-Wei, Yao Hong-Bo, Zhang Yuan-Zheng, Jiang Wei-Bo, Wu Yong-Hui, Zhang Ya-Ju, Ao Tian-Yong, Zheng Hai-Wu.Self-powered sensing based on triboelectric nanogenerator through machine learning and its application. Acta Physica Sinica, 2022, 71(7): 078702.doi:10.7498/aps.71.20211632
[11]	Chen Jiang-Zhi, Yang Chen-Wen, Ren Jie.Machine learning based on wave and diffusion physical systems. Acta Physica Sinica, 2021, 70(14): 144204.doi:10.7498/aps.70.20210879
[12]	Lin Jian, Ye Meng, Zhu Jia-Wei, Li Xiao-Peng.Machine learning assisted quantum adiabatic algorithm design. Acta Physica Sinica, 2021, 70(14): 140306.doi:10.7498/aps.70.20210831
[13]	Wei Lian-Suo, Li Hua, Wu Di, Guo Yuan.Clock synchronization error compensation algorithm based on BP neural network model. Acta Physica Sinica, 2021, 70(11): 114203.doi:10.7498/aps.70.20201641
[14]	Yang Zi-Xin, Gao Zhang-Ran, Sun Xiao-Fan, Cai Hong-Ling, Zhang Feng-Ming, Wu Xiao-Shan.High critical transition temperature of lead-based perovskite ferroelectric crystals: A machine learning study. Acta Physica Sinica, 2019, 68(21): 210502.doi:10.7498/aps.68.20190942
[15]	Wang Yue, Liang Yan-Sheng, Yan Shao-Hui, Cao Zhi-Liang, Cai Ya-Nan, Zhang Yan, Yao Bao-Li, Lei Ming.Axial multi-particle trapping and real-time direct observation. Acta Physica Sinica, 2018, 67(13): 138701.doi:10.7498/aps.67.20180460
[16]	Fan Shuang, Zhang Ya-Ping, Wang Fan, Gao Yun-Long, Qian Xiao-Fan, Zhang Yong-An, Xu Wei, Cao Liang-Cai.Gerchberg-Saxton algorithm and angular-spectrum layer-oriented method for true color three-dimensional display. Acta Physica Sinica, 2018, 67(9): 094203.doi:10.7498/aps.67.20172464
[17]	Huang Ke, Li Song, Ma Yue, Tian Xin, Zhou Hui, Zhang Zhi-Yu.Theoretical model and correction method of range walk error for single-photon laser ranging. Acta Physica Sinica, 2018, 67(6): 064205.doi:10.7498/aps.67.20172228
[18]	Yu Zhen-Tao, Lü Jun-Wei, Bi Bo, Zhou Jing.A vehicle magnetic noise compensation method for the tetrahedron magnetic gradiometer. Acta Physica Sinica, 2014, 63(11): 110702.doi:10.7498/aps.63.110702
[19]	Zeng Zhe-Zhao.Feedback compensation control on chaotic system with uncertainty based on radial basis function neural network. Acta Physica Sinica, 2013, 62(3): 030504.doi:10.7498/aps.62.030504
[20]	Han Min, Xu Mei-Ling.A hybrid prediction model of multivariate chaotic time series based on error correction. Acta Physica Sinica, 2013, 62(12): 120510.doi:10.7498/aps.62.120510

Catalog

Vol. 73, Issue 16 - 2024-08-20

Metrics

Abstract views:303
PDF Downloads:19
Cited By:0

Search

Article