Laser–Arc Welding Adaptive Model of Multi-Pre-Welding Condition Based on GA-BP Neural Network - Zesheng Wu; Zhaodong Zhang; Gang Song (Metals)
In large welding structures, maintaining a uniform assembly condition and machined dimension in the pre-welding groove is challenging. The assembly condition and machined dimension of the pre-welding groove significantly impact the selection of the welding parameters. In this study, laser–arc hybrid welding is used to perform butt welding on 6 mm Q345 steel in various assembly conditions, and we propose an adaptive model of the BP neural network optimized by a genetic algorithm (GA) for laser–arc welding. By employing the GA algorithm to optimize the parameters of the neural network, the relationship between the pre-welding groove parameters and welding parameters is established. The mean square error (MSE) of the GA-BP neural network is 0.75%. It is verified via experiments that the neural network can predict the welding parameters required to process a specific welding morphology under different pre-welding grooves. This model provides technical support for the development of intelligent welding systems for large and complex components.