用强化学习实时调控激光焊接，自动适应材料表面差异。
Reinforcement Learning on Reconfigurable Hardware: Overcoming Material Variability in Laser Material Processing
- 在FPGA上实现强化学习，实时动态调整激光功率
- 粗糙表面性能提升23%，混合表面提升7%
- 无需预设参数或奖励设计，适合工业现场部署
由于材料特性与表面状态差异，激光加工过程的质量一致性难以保证。现有自动化方法多依赖预设目标或仅限于仿真环境。为此，我们提出一种基于现场可编程门阵列（FPGA）的实时强化学习控制方法，用于激光工艺优化。在不同表面粗糙度的不锈钢焊接实验中，该方法无需奖励工程或先验设置，可自主学习适配每种表面特征的最优功率曲线。结果表明，相比人工设定的最佳恒定功率策略，该方法在粗糙表面表现提升最高达23%，混合表面提升7%。该技术为激光加工的自动化与智能化提供了新路径，具有广泛工业应用前景。
原文摘要 · Abstract (English)
Ensuring consistent processing quality is challenging in laser processes due to varying material properties and surface conditions. Although some approaches have shown promise in solving this problem via automation, they often rely on predetermined targets or are limited to simulated environments. To address these shortcomings, we propose a novel real-time reinforcement learning approach for laser process control, implemented on a Field Programmable Gate Array to achieve real-time execution. Our experimental results from laser welding tests on stainless steel samples with a range of surface roughnesses validated the method's ability to adapt autonomously, without relying on reward engineering or prior setup information. Specifically, the algorithm learned the correct power profile for each unique surface characteristic, demonstrating significant improvements over hand-engineered optimal constant power strategies -- up to 23% better performance on rougher surfaces and 7% on mixed surfaces. This approach represents a significant advancement in automating and optimizing laser processes, with potential applications across multiple industries.
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