An investigation into an intelligent system for predicting bead geometry in GMA welding process

Gas metal arc (GMA) welding process has been chosen as a metal joining technique due to the wide range of usable applications, cheap consumables and easy handling. The welding quality is generally controlled by the welding parameters. To achieve a high level of welding performance and quality, a suitable algorithm is required to fully understand the influence of welding parameters on bead geometry in the GMA welding process. In this paper, we develop an intelligent system in GMA welding processes using MATLAB/SIMULINK software. Based on multiple regressions and a neural network, the mathematical models are derived from extensive experiments with different welding parameters and complex geometrical features. Graphic displays represent the resulting solution on the bead geometry that can be employed to further probe the model. The developed system enables to input the desired weld dimensions and select the optimal welding parameters. The experimental results were proved the capability of the developed system to select the welding parameters in GMA welding process according to complex external and internal geometrical features of the substrate.