用强化学习自动优化激光切割参数，提升精度并大幅减少耗时和材料浪费。
Reinforcement Learning-Based Laser Cutting Machine Parameter Optimization
- 基于Q-learning与贪心策略动态调整激光功率和焦距等参数。
- 相比现有方法，优化步骤减少12.5%，加工时间缩短81.8%。
- 可自适应新材料，适合工业场景中需快速调参的激光切割任务。
光学薄膜的高精度激光切割依赖于对焦距、激光功率等参数的精细调节，传统试错法效率低且不精准。本文提出强化学习激光切割算法RL²C，采用Q-learning结合epsilon-greedy策略，动态优化切割参数，显著降低切口锥度与膜材损耗。该算法还引入动态环境空间自适应机制，可在多批次实验中应对新状态。实验表明，与多种基于强化学习的优化方法相比，RL²C所需优化步数减少最多达12.5%，处理时间缩短最多达81.8%。研究验证了强化学习在工业激光切割中的潜力，可提升切割质量、降低时间成本与材料浪费，并减少人工干预。
原文摘要 · Abstract (English)
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.
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