Integration of RSM with Grey based Taguchi Method for optimization of pulsed TIG welding process parameters

This research paper focused on process parameters optimization of Tungsten Inert Gas (TIG). Taguchi method is very effective and efficient to solve the process parameter optimization problem to the find optimum processing parameter, but it is found that the traditional Taguchi method is not sufficient to solve the problem with multi response problem to get optimum results and to meet the global challenges of the manufacturer as it is impractical to obtain more than one optimal processing parameter through Taguchi. In order to survive in the global market it is necessary to used advanced and modified optimization techniques. To intensify the efficiency of Taguchi method, it is absolutely necessary to integrate it with the other optimization approach. However, it is important to integrated Taguchi method with Grey Retion Analysis (GRA) to solve multi response optimizaton promblem futher Taguchi method is integrated with Response Surface Methodology (RSM) to determine optimum value of each parameters. Using orthogonal array L9 experiments were performed. Experimental data are used to obtain signal to noise ratio by using Minitabl 8 and then response surface plot is developed to obtain optimum level for every input parameter. Further, the process is integrated with RSM to developed mathematical model by using design expert 11 statistical software through full-factorial central composite design (CCD). RSM is a useful statistical tool for process optimization to obtain the required quality of weld. The Surface plot obtained from Design Expert Software 11 represent that the actual value of response i.e. depth of penetration (DOP) and width of penetration (WOP) versus the predicted value of response are close to each other. The higher value of DOP and WOP represents higher strength; analysis of variance (ANOVA) gives the highest value of F-ratio of welding speed in DOP and WOP, this result obtained from RSM showed that welding speed is the most influencing parameter which affects response characteristics. (C) 2019 Elsevier Ltd. All rights reserved.