Reinforcement Learning-Based Laser Cutting Machine Parameter Optimization

2026-08-11

AI Summary

This study addresses the inefficiency and poor generalization of traditional trial-and-error approaches in laser cutting of optical thin films. To overcome these limitations, the authors propose RL²C, a reinforcement learning framework that integrates Q-learning with an ε-greedy strategy and incorporates a dynamic state-space expansion mechanism to adaptively tune critical parameters such as focal length and laser power. This work represents the first application of reinforcement learning with dynamic environmental adaptability to industrial laser cutting, significantly enhancing optimization efficiency and cross-material generalization. Experimental results demonstrate that RL²C reduces the number of optimization steps by 12.5% and processing time by 81.8% compared to existing methods, while effectively minimizing cut taper and film loss.

Abstract

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.

Problem

• laser cutting
• parameter optimization
• optical films
• reinforcement learning
• cutting accuracy

Innovation

• Reinforcement Learning
• Laser Cutting Optimization
• Q-learning
• Dynamic Environment Adaptation
• Parameter Tuning