AI-driven real-time optimization of laser welding: A low-latency approach for high quality welds

In the precision-driven domain of laser welding, achieving consistent high-quality welds while maintaining process efficiency poses a significant challenge. This paper introduces a low-latency AI-based framework designed to optimize the laser welding process in real time, addressing this challenge through advanced monitoring and control. The system comprises two key components: an AI-based monitoring system and an AI-based controller. Utilizing data from a high-speed camera and a high-speed microphone, the monitoring system integrates multiple AI models and data fusion to predict four critical quality measures. These include surface quality parameters and subsurface characteristics, such as weld depth and bonding width, with weld depth predictions achieving less than 10% error. The control system leverages these real-time assessments to compute optimal process parameters, ensuring continuous process optimization. It manages seven control parameters, including x–y beam oscillation and power modulation, with monitoring inference in approximately 2–3 ms, control computation in approximately 1 ms, and a total reaction time of approximately 10–25 ms. A stochastic digital twin simulator facilitated system testing and tuning prior to deployment. The novelty of this work lies in achieving low-latency operation under high complexity (four quality measures, seven control parameters). In surrogate-based stochastic closed-loop simulations calibrated from copper-electrode welding experiments, the proposed controller reduced the simulated quality deviation from target values by 29%–48%, depending on the quality measure.