发表机构
Chungbuk National University; NPS CO., LTD.（忠北国立大学; NPS有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对激光切割参数优化的传统方法效率低、精度差的问题，提出RL²C算法，实验显示其优化步骤减少12.5%、处理时间缩短81.8%，可提升激光切割质量、降低浪费，展现强化学习在工业激光切割中的应用潜力。
AI 中文摘要
在光学薄膜的激光切割中，要达到高精度，需根据每种薄膜的特定属性仔细调整焦距、激光功率束等参数。传统基于试错的方法用于为各类薄膜找到最合适的切割参数，但这类方法速度慢且精度低。为解决该问题，本文提出了RL²C（Reinforcement Learning for Laser Cutting，激光切割强化学习）算法，其采用带ε-贪心策略的Q学习来动态优化切割参数，可显著减小锥度尺寸和薄膜浪费。此外，RL²C还融入了动态环境空间适应性机制，使其能在多批次实验的学习过程中适应遇到的新状态。实验结果表明，与多种基于强化学习的优化方法相比，RL²C找到最优切割参数所需的步骤更少、时间更短。具体而言，与现有方法相比，RL²C最多可减少12.5%的优化步骤和81.8%的处理时间。本研究通过提升切割质量、减少时间与薄膜浪费、最小化人工干预，展现了强化学习在工业激光切割流程中的应用潜力。
英文摘要
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 (RL$^{2}$C) algorithm, which uses Q-learning with an epsilon-greedy policy to dynamically optimize cutting parameters, significantly reducing taper size and film wastage. Additionally, RL$^{2}$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 RL$^{2}$C requires fewer steps and less time to find optimal cutting parameters compared to various RL-based optimization methods. Specifically, RL$^{2}$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.
Comments21 pages, 8 figures. Accepted manuscript published in Journal of Intelligent Manufacturing (2025). DOI: 10.1007/s10845-025-02619-z
Journal refJournal of Intelligent Manufacturing 37 (2025) 1813-1828
