Modeling of Weld Bead Geometry Using Adaptive Neuro-Fuzzy Inference System (ANFIS) in Additive Manufacturing

Additive Manufacturing describes the technologies that can produce a physical model out of a computer model with a layer-by-layer production process. Additive Manufacturing technologies, as compared to traditional manufacturing methods, have the high capability of manufacturing the complex components using minimum energy and minimum consumption. These technologies have brought about the possibility to make small pieces of raw materials in the shortest possible time without the need for a mold or tool. One of the technologies used to make pieces of the layer-by-layer process is the Gas Metal Arc Welding (GMAW). One of the basic steps in this method of making parts is the prediction of bead geometry in each pass of welding. In this study, taking into account the effective parameters on the geometry of weld bead, an empirical study has been done in this field. For this purpose, three parameters of voltage, welding speed and wire feeding rate are considered as effective parameters on the welding geometry of the process. Width and height of the bead are also determined by the parameters of the geometry of the weld according to the type and application of the research as output parameters are considered. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used to create an adaptive model between input process data and parameters of weld bead geometry. The least squares mean error is used to evaluate the model. The predicted results by the model have a good correlation with the experimental data.