Reinforcement learning-based laser cutting machine parameter optimization

Pham Khanh Quan

doi:10.1007/s10845-025-02619-z

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Reinforcement learning-based laser cutting machine parameter optimization

2025-05-21

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DOI：10.1007/s10845-025-02619-z

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Abstract

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Achieving high accuracy in laser-based cutting of optical films requires careful tuning of parameters such as focal length and laser power beam, adjusted according to the specific properties of each film type. Trial-and-error based traditional methods are used to find the most suitable cutting parameters for various films, but they are slow and inaccurate. To address this issue, this paper presents the Reinforcement Learning for Laser Cutting (RL2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {RL}<^>{2}$$\end{document}C) algorithm, which uses Q-learning with an epsilon-greedy policy to dynamically optimize cutting parameters, significantly reducing taper size and film wastage. Additionally, RL2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {RL}<^>{2}$$\end{document}C incorporates a dynamic environment space adaptability mechanism to allow it to adapt to new states encountered during the learning process over multiple batches of experiments. Experimental results demonstrate that RL2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {RL}<^>{2}$$\end{document}C requires fewer steps and less time to find optimal cutting parameters compared to various RL-based optimization methods. Specifically, RL2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {RL}<^>{2}$$\end{document}C reduces the number of optimization steps by up to 12.5% and processing time by up to 81.8% compared to existing methods. This study demonstrates the potential of RL in industrial laser-cutting processes by improving cut quality, reducing time and film wastage, and minimizing manual interventions.

Keywords:

Reinforcement learning

Q-learning

Parameter optimization

Dynamic environment space