The objective of the current work is to provide sufficient knowledge of the welding industry to allow the optimization of the process so as to achieve best final properties. The welding process is complex with many interacting variables controlling the procedure. In addition, not all of the physics of the process, particularly with regard to the factors which control mechanical properties, are well understood. It is unlikely that a full analytical model can be prepared and physically based numerical techniques will also suffer from a lack of basic understanding. A model for use as an optimization tool will thus have to proceed along different lines. Fortunately, this work has produced a wealth of experimental observations, which can be used for optimization. In this research, several welds were fabricated using Gas Metal Arc Welding process at different welding conditions. The technique of Response Surface Methodology was applied to develop a mathematical model to analyze various effects of GMAW parameters on the and mechanical properties such as yield strength and ultimate tensile strength of Duplex Stainless Steel weldments.
Dual-phase duplex stainless steel (DSS) has shown outstanding strength. Joining DSS alloy is challenging due to the formation of embrittling precipitates and metallurgical changes during the welding process. Generally, the quality of a weld joint is strongly influenced by the welding conditions. Mathematical models were developed to achieve high-quality welds and predict the ideal bead geometry to achieve optimal mechanical properties. Artificial neural networks are computational models used to address complex nonlinear relationships between input and output variables. It is one of the powerful modeling techniques, based on a statistical approach, presently practiced in engineering for complex relationships that are difficult to explain with physical models. For this study robotic GMAW welding process manufactured the duplex stainless steel welds at different welding conditions. Two tensile specimens were manufactured from each welded plate, resulting in 14 tensile specimens. This research focuses on predicting the yield strength, tensile stress, elongation, and fracture location of duplex stainless steel SAF 2205 welds using back-propagation neural networks. The predicted values of tensile strength were later on compared with experimental values obtained through the tensile test. The results indicate <2% of error between observed and predicted values of mechanical properties when using the neural network model. In addition, it was observed that the tensile strength values of the welds were higher than the base metal and that this increased when increasing the arc current. The welds’ yield strength and elongation values are lower than the base metal by 6%, ~ 9.75%, respectively. The yield strength and elongation decrease might be due to microstructural changes when arc energy increases during the welding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.