Determination of laser welding process parameters through a combined neural networks and computational fluid dynamics approach

In this paper weld pool shapes of type 304 stainless steel obtained through computational fluid dynamics simulations (CFD) will be used to train artificial neural networks. The neural network is then used to determine the experimental parameters of the ND:YAG laser system on 304 steel by feeding it with the parametrized, experimentally obtained weld pool parameters. The output of the neural network are the process parameters like laser spot diameter and laser power on the surface of the weld pool. These parameters are very difficult to determine accurately through experiments but are necessary inputs for CFD simulations of the weld pool. Accurate CFD simulation is required for simulation and modeling of the solidified micro structure of the material.