A study on the quality improvement of robotic GMA welding process

With the advance of the robotic welding process, procedure optimisation that selects the welding procedure and predicts bead geometry that will be deposited has increased. A major concern involving procedure optimisation should define a welding procedure that can be shown to be the best with respect to some standard, and chosen combination of process parameters, which give an acceptable balance between production rate and the scope of defects for a given situation.

This paper represents a new algorithm to establish a mathematical model for predicting top-bead width through a neural network and multiple regression methods, to understand relationships between process parameters and top-bead width, and to predict process parameters on top-bead width in robotic gas metal arc (GMA) welding process. Using a series of robotic GMA welding, additional multi-pass butt welds were carried out in order to verify the performance of the multiple regression and neural network models as well as to select the most suitable model. The results show that not only the proposed models can predict the top-bead width with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models.