Attaining optimized A-TIG welding parameters for carbon steels by advanced parameter-less optimization techniques: with experimental validation

Abstract

Enhanced penetration potential of activated TIG (A-TIG) welding process over conventional TIG welding has made the former process preferred for welding in recent times. The quality and shape of the weld in A-TIG are not only influenced by the chemical composition of the flux, but also by the selection of welding parameters. As a variety of process parameters influence the results, a proper understanding of process performance and identification of favorable welding conditions (optimum setting of process parameters) are indeed necessary to improve quality. The present work highlights the application potential of multi-response optimization route by integrating response surface methodology with the JAYA optimization algorithm, particularly for optimizing the A-TIG welding process parameters for carbon steels. Systematic experiments were carried out considering welding current, arc gap and travel speed as input parameters, whereas the depth of penetration, depth-to-width ratio, heat input and total width of heat-affected zone were considered as output performance characteristics. An attempt has been made in the current study to recognize the precise setting of selected input process parameters for simultaneous optimization of the aforementioned performance characteristics. The result of JAYA algorithm has also been compared with the teaching-learning-based optimization technique. While fairly similar results were achieved, the implementation of JAYA algorithm was computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the response parameters as well as the proposed optimization framework are accurate tools in A-TIG welding research.

Effect of backing plate on mechanical and microstructural properties of tungsten inert gas welded AA 6063 butt joints

“…It is fundamental that heat input to a welding process will play a vital role in deciding the quality characteristics of a weldment. 18,19 Therefore, the net heat input during the process for each weld was calculated for identifying correct samples to be evaluated using the formula 20 HeatInput kJ mm (2.08 kJ//mm), low, medium and high heat welded joints were identified and these samples were extracted to a size of 30 x 15 x 6 mm 3 in the transverse direction to the welded plate to explore the macro and microstructural characteristics.…”

Welding investigation of Hastelloy C-276 and parametric optimization using integrated MRA-TLBO algorithm during A-TIG process

“…Additionally, it was concluded that, as compared to conventional TIG welding, ATIG and FA-TIG welded samples using SiO 2 flux exhibited an increase in depth of penetration of 120%-130% and an increase in depth width ratio of 190%. Vora et al [13] used the JAYA algorithm to demonstrate the application potential of numerous response optimisation procedures utilising an integrated surface approach on a 6 mm thick plate. In their research, they identified various welding characteristics, including depth of penetration (DOP) and depth to width ratio (DWR) of HAZ, as well as input parameters like current, speed, and arc gap.…”

Welding investigations on mechanical property and microstructure of TIG and A-TIG Weld of Hastelloy C-276

“…Patel et al [19] studied bead-on plate welding of Hastelloy C-22, 6 mm thick coupons by several A-TIG variations using four types of fluxes, including (SiO 2 , Al 2 O 3 ) as acidic oxides and (Fe 2 O 3 , and CUO) as basic oxides. It was noticed that welded samples (ATIG and FA-TIG) employing SiO 2 flux enhanced the depth of penetration by 120%-130% and the depth breadth ratio by 190% when compared with conventional TIG welding.Vora et al [20] applied response optimization processes by an integrated surface procedure using the JAYA algorithm with an A-TIG welding process framework on a 6 mm thick plate. In their experiments, they reported various welding parameters like-current, speed, arc gap, and output parameters like depth of penetration (DOP), depth-to-width ratio (DWR), etc They revealed that similar results were achieved when the JAYA algorithm was compared with TLBO (teaching-learning-based optimization).…”