Autonomous calibration of OCT lag distance for the monitoring of the laser weld depth

Demand for online process monitoring and robotic automation is increasing. This comes as a result of the rapidly growing importance of e-mobility applications that require a precise definition and control of the laser weld depth. Optical coherence tomography (OCT) has progressively emerged as a key enabling technology capable of understanding capillary behavior and monitoring the depth in real-time in laser welding processes. The reliability of the OCT acquisitions, however, depends on the careful calibration of the OCT within the capillary. In particular, where manual calibration demonstrated to be unreasonably laborious, expertise-requiring, and repetitive, robotic automation emerged as the potential to assist in an efficient monitoring of the weld depth with minimal manual intervention. This study presents a novel autonomous approach for the laser weld depth acquisition using OCT. Focal distance recognition, synchronization of the scanner head-OCT, lag distance extraction, and keyhole depth in-situ monitoring are all performed using python screen automation tools. The spatial and temporal positioning of the OCT beam with respect to the laser welding beam is determined through an iterative approach, fully executed without manual intervention. The fully automated experiments achieved optimum fully executed results, faster than human process development.