Regression tree and neuro-fuzzy approach to system identification of laser lap welding

Laser lap welding quality is a nonlinear response based on a host of material and process variables. This is inclusive of both categorical and numeric variables. An adaptive neuro fuzzy inference system in combination with a classification and regression tree is used to characterize this process. This characterization is shown to be more accurate than that achieved by a nonlinear regression model.