A review of automation of laser optics alignment with a focus on machine learning applications
Ildar Rakhmatulin, Donald Risbridger, Richard Carter, M. J. Daniel Esser, Mustafa Suphi Erden
Abstract
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).