Xie Yunhui, Praeger Matthew, Grant-Jacob James A, Eason Robert W, Mills Ben
Opt Express. 2022 Jun 6;30(12):20963-20979. doi: 10.1364/OE.454793.
Laser processing techniques such as laser machining, marking, cutting, welding, polishing and sintering have become important tools in modern manufacturing. A key step in these processes is to take the intended design and convert it into coordinates or toolpaths that are useable by the motion control hardware and result in efficient processing with a sufficiently high quality of finish. Toolpath design can require considerable amounts of skilled manual labor even when assisted by proprietary software. In addition, blind execution of predetermined toolpaths is unforgiving, in the sense that there is no compensation for machining errors that may compromise the quality of the final product. In this work, a novel laser machining approach is demonstrated, utilizing reinforcement learning (RL) to control and supervise the laser machining process. This autonomous RL-controlled system can laser machine arbitrary pre-defined patterns whilst simultaneously detecting and compensating for incorrectly executed actions, in real time.
激光加工技术，如激光加工、打标、切割、焊接、抛光和烧结，已成为现代制造业中的重要工具。这些工艺中的关键步骤是获取预期设计并将其转换为运动控制硬件可用的坐标或刀具路径，从而实现高效加工并获得足够高的加工质量。即使有专有软件辅助，刀具路径设计仍可能需要大量熟练的体力劳动。此外，预定刀具路径的盲目执行是不可原谅的，因为对于可能影响最终产品质量的加工误差没有补偿。在这项工作中，展示了一种新颖的激光加工方法，利用强化学习（RL）来控制和监督激光加工过程。这个自主的RL控制系统可以对任意预定义图案进行激光加工，同时实时检测和补偿错误执行的动作。
