A review of automation of laser optics alignment with a focus on machine learning applications
A review of automation of laser optics alignment with a focus on machine learning applications
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DOI：
10.1016/j.optlaseng.2023.107923
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发表时间：
2024-02
影响因子：
4.6
通讯作者：
I. Rakhmatulin;D. Risbridger;R. M. Carter;M. J. D. Esser;M. S. Erden
中科院分区：
文献类型：
--
作者：
I. Rakhmatulin;D. Risbridger;R. M. Carter;M. J. D. Esser;M. S. Erden
在工业和实验室激光系统中，各种光学元件的定位涉及复杂的过程，这些过程非常耗时。近年来，机器学习已被证明是通用控制自动化和调整任务的可靠工具。然而，机器学习尚未在需要非常熟练的劳动力来组装和调整高精度设备的特定任务中得到广泛应用，例如在光子学领域内大量激光系统中实现的各种光学元件。本文综述了自动化和机器学习在反射镜位置调整、三角测量、最佳激光参数的选择以及各种光学元件的其他控制参数等方面的研究。提出了利用机器学习建立工业和实验室激光系统的研究方向，并提出了相应的建议。本文的综述是基于系统评价和荟萃分析的首选报告项目（PRISMA）中提出的建议。
In industrial and laboratory-based laser systems there are complicated processes involved in the positioning of various optical components and these processes are time consuming. Machine learning has proven itself in recent years as a reliable tool in general control automation and adjustment tasks. However, machine learning has not yet found wide-spread application in specific tasks that require very skilled workforces to assemble and adjust high-precision equipment, such as the wide array of optical components that are implemented across vast numbers of laser systems within the field of photonics. This review provides a comprehensive summary of research in which automation and machine learning have been used in the processes of mirror positional adjustment, triangulation, and the selection of optimal laser parameters alongside other control parameters of various optical components. Promising research directions are presented with corresponding proposals on the use of machine learning for the task of setting up industrial and laboratory laser systems. The review in this paper was based on the recommendations presented in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).