Prediction of laser-spot-weld shape by numerical analysis and neural network

The finite element method (FEM) and neural network were applied for predict ing the bead shape in laser spot welding of type 304 thin stainless steel s heets. The parameters of pulsed Nd:YAG laser spot welding such as pulse ene rgy, pulse duration, sheet metal thickness, and gap between sheets were var ied for various experiments and numerical simulations. The penetration dept h and nugget size of spot welds measured for specimens without gap were com pared with the calculated results to verify the proposed finite element mod el. Sheet metal thickness, gap size, and bead shape of the workpiece withou t gap were selected as the input variables for the back-propagation learnin g algorithm of the neural network, while the bead shape of the workpiece wi th and without gap was considered as its output variable. Various combinati ons of stainless steel sheet metal thickness were considered to calculate t he laser-spot-weld bead shape of the workpiece without gap, which was then used as the input variable of neural network to predict the bead shape for various gap sizes. This combined model of finite element analysis and neura l network could be effectively applied for the prediction of bead shapes of laser spot welds, because the numerical analysis of laser spot welding for the workpiece with gap between two sheets is highly limited.