Microstructure Analysis and Multi-objective Optimization of Pulsed TIG Welding of 316/316L Austenite Stainless Steel

Analysis and Optimization of Bead Geometry by Using Response Surface Methodology

“…The correlation between process parameters and output response was obtained by using CCD The second-order polynomial regression equation fitted between the output response and the input process parameter. From the ANOVA result shown in Table 4, it has been found adequacy of the model is suitable to analyze the experimental value [2,6].…”

“…The butt joint was made from AISI 316 stainless steel sheets with dimensions of 100 Â 75 Â 4 mm by used pulsed TIG welding [4]. This experiment's input parameters are peak current, base current, pulse frequency, and pulse on time [2]. Input parameters with their levels are given in Table 1.…”

“…Examine the combined effect of four different input parameters on bead geometry and mechanical properties to optimize the process parameter of pulse TIG welding and drive a mathematical model. Five levels, four-parameter CCD which include 24 = 16 factorial points plus 6 central points and 2 Â 4-star points (24 + 2*4 + 6) [2,5], with a total of 30 experiments were made in this investigation as shown in Table 2. The framework for the four factors ranged between five levels, Àγ, α,+β, and +γ).…”

“…The experiment was carried out using various parameter combinations, as shown in Table 3. The CCD experiment results were fitted to the polynomial regression equation created by Design Expert Software 18.0 [2,6].…”

“…RSM is a set of mathematical and statistical methodologies for assessing problems in which multiple independent factors influence a dependent variable or response, to optimize the answer. RSM facilitates the examination of the interaction between experiment variables within the range under consideration, allowing for a better knowledge of the process while lowering experiment time and cost [2,3].…”