Process modeling with neural networks for pulsed GMAW braze welds

In this work, an improved weld-bead geometry and reduced sheet metal distortion were desired in a weld-brazing process used to finish exterior sheet metal prior to painting. Numerical modeling techniques are widely used to model welding processes, but accurate methods for modeling pulsed-welding processes are lacking. Instead, a continuous empirical model of the welding process was developed using a statistically designed experiment and a neural network for data analysis. Graphical methods were used to simulate the process, and robust parameter design techniques were used to analyze response surfaces produced by the model. Using the response surfaces, possible welding procedures were generated and tested. A new process that resulted in improved joint quality was implemented in production.