Dimensional Control of Laser Direct Energy Deposition Forming Based on Kriging Model and Reinforcement Learning

In view of the defect of the traditional proportional-integral-derivative control method, which needs to reset the controller parameters as the process parameters change, this study employs a reinforcement learning correction framework based on the Kriging model, where the framework is specifically designed to predict and control melt pool dimensions, thereby eliminating the need for parameter tuning. Through iterative learning of the effects of process parameters on melt pool dimensions, the framework corrects the embedded Kriging prediction model by enhancing its predictive performance and yielding more optimized process parameters. Experimental results demonstrate that this framework can mitigate the melt pool backflow effect, proficiently manage width errors, reduce cumulative height errors in formed components, and significantly enhance the dimensional accuracy of laser direct energy deposition components.