A neuro-fuzzy approach to select welding conditions for welding quality improvement in horizontal fillet welding

Abstract

Abstract Many researchers have developed algorithms to control welding parameters for a desired weld bead geometry. Unstable welding conditions induce an unsound bead, resulting in poor mechanical properties at welded joints. Generally, the dominant variables affecting weld bead geometry are welding current, arc voltage, and welding speed. In practice, it is difficult to determine the proper combination of welding conditions because of excessive nonlinear and complex characteristics of welding processes. The relationship between welding conditions and weld defects cannot be easily represented by mathematical models, and it is difficult to predict weld bead geometry resulting from welding conditions. A fuzzy rule based method and neural network method are proposed: the neural network method predicts welding conditions appropriate for the desired weld bead geometry, and the fuzzy rule based method chooses appropriate welding conditions for avoiding weld defects such as undercut and overlap in horizontal fillet welding. Performance of the proposed neuro-fuzzy system was evaluated through experiments, which showed that the system can effectively check and adjust welding conditions in regard to weld defects.