Smart closed-loop control of laser welding using reinforcement learning
Tri Le Quang, Bastian Meylan, Giulio Masinelli, Fatemeh Saeidi, Sergey Shevchik, Farzad Vakili Farahani, Kilian Wasmer
Abstract
The present work demonstrates an adaptive closed-loop control for laser welding processes. Based on feedback signals from a sensing system, the controller interacts with the laser to adapt the processing parameters to achieve or maintain the target welding quality. The controller is constructed based on a model-free reinforcement learning approach, namely Q-learning. This algorithm allows autonomous learning of the control law independently from the starting conditions as well as any prior knowledge of the process dynamics. The controller's performance is demonstrated in both a well-controlled lab environment and more unpredictable industrial situations. For the demonstration, the control system is allowed to vary the laser power, and the feedback signal is given by an industrial laser process control unit (Coherent SmartSense+) using an optical sensor. The time needed to train the control system is approximately five and twenty minutes for the well-controlled and the industrial situations, respectively.