Design of experiment based statistical approaches to optimize submerged arc welding process parameters

Joining of metals is very useful concept which is being utilized since Bronze Age and then gradual advancement gave rise to development of modern welding. And now welding is increasingly used in the fields of fabrication, manufacturing and construction. But productivity is the main concern in many manufacturing and industrial welding applications. Therefore selection of a welding process and its variables/parameters without sacrificing weld quality with respect to productivity and its quality is very important because an optimum blend of parameters which inevitably develop minimum or no defect will result in high productivity. For this study Submerge Arc Welding (SAW) process is selected for optimization because this versatile welding process is the first choice whenever good productivity with high quality requires in fabrication and manufacturing of Marine & pressure vessels, pipelines and offshore structures. Here Signal to noise (S/N) ratio analyses are used to find significant effects of key parameters on selected responses and then for optimization design of experiment based both quality loss function (OFM) and desirability function along with variance analyses by ANOVA are utilized. Moreover codes and standards provide a range for weld process parameters but author experienced that still there is a window to further optimize these parameters to produce the quality weld. Therefore this study is also useful to contribute in welding related research work by enhancing the knowledge of welding process and its analysis by utilizing advance statistical optimization techniques to find optimum zone within the acceptable zone from Code & Standard based tolerance Zone.