A STUDY ON WELDING CHARACTERISTICS OF MATERIALS FOR AUTOMOBILE SUB-FRAME

Arc welding process, one of the most common joining processes in the metal industries was widely applied for facilities from Nano machines to highly-automated factories. More recently, products of piles and columns to support wind turbines has grown significantly in importance. A new algorithm that predicts the optimal welding parameters on a given bead geometry and accomplishes the desired mechanical properties of the weldment in order to make the automated GMA(Gas Metal Arc) welding process should be required. The developed algorithm should also make use of a wide range of material thicknesses and be applicable for all welding positions. In addition, the algorithm must be available in the form of mathematical equations which can be programmed easily to the robot and give a high degree of confidence in predicting the bead dimensions. In this study, two regression models employing global regression analysis and cluster-wise regression analysis are proposed to be applicable to estimate the optimal welding parameters on the bead penetration area. For development of the proposed regression models, an attempt has been made to apply for a several methods. A full factorial design studying the effects of welding parameters on bead penetration area as a function of key output parameters with the lab-joint weld in the automated GMA welding process was carried out. The fitting of these models were checked and compared by using a variance test (ANOVA). Also the performance of the prediction of bead penetration area using the developed regression a model was verified the additional experiments