Modeling and optimization of features of bead geometry including percentage dilution in submerged arc welding using mixture of fresh flux and fused slag

Quality has now become an important issue in today’s manufacturing world. Whenever a product is capable of conforming to desirable characteristics that suit its area of application, it is termed as high quality. Therefore, every manufacturing process has to be designed in such a way that the outcome would result in a high quality product. The selection of the manufacturing conditions to yield the highest desirability can be determined through process optimization. Therefore, there exists an increasing need to search for the optimal conditions that would fetch the desired yield. In the present work, we aim to evaluate an optimal parameter combination to obtain acceptable quality characteristics of bead geometry in submerged arc bead-on-plate weldment on mild steel plates. The SAW process has been designed to consume fused flux/slag, in the mixture of fresh flux. Thus, the work tries to utilize the concept of ‘waste to wealth’. Apart from process optimization, the work has been initiated to develop mathematical models to show different bead geometry parameters, as a function of process variables. Hence, optimization has been performed to determine the maximum amount of slag--flux mixture that can be used without sacrificing any negative effect on bead geometry, compared to the conventional SAW process, which consumes fresh flux only. Experiments have been conducted using welding current, slag-mix percentage and flux basicity index as process parameters, varied at four different levels. Using four³ full factorial designs, without replication, we have carried out welding on mild steel plates to obtain bead-on-plate welds. After measuring bead width, depth of penetration and reinforcement; based on simple assumptions on the shape of bead geometry, we calculated other relevant bead geometry parameters: percentage dilution, weld penetration shape factor, weld reinforcement form factor, area of penetration, area of reinforcement and total bead cross sectional area. All these data have been utilized to develop mathematical models between predictors and responses. Response surface methodology (RSM), followed by the multiple linear regression method, has been applied to develop these models. The effects of selected process parameters on different responses have been represented graphically. Finally grey relational analysis coupled with the Taguchi method (with Taguchi’s orthogonal array) has been applied for parametric optimization of this welding technique. Confirmatory experiments have been conducted to verify optimal results.