Reinforcement Learning-Based Laser Cutting Machine Parameter Optimization
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
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 (RL2C) algorithm, which uses Q-learning with an epsilon-greedy policy to dynamically optimize cutting parameters, significantly reducing taper size and film wastage. Additionally, RL2C 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 RL2C requires fewer steps and less time to find optimal cutting parameters compared to various RL-based optimization methods. Specifically, RL2C 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 26 references that are in the map. First posted on arXiv 11 Aug 2026.
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