Sensitivity analysis of process parameters on tensile properties in Plasma Arc Welding of AA8011-H24 aluminium alloys and Ti3Al2.5V Titanium alloys using Response Surface Methodology

In welding processes, many factors contribute in achieving a required quality of the welds. Those factors are numerous and they may interact with each other, affecting response parameters such as welding penetration and the Heat Affected Zone (HAZ) size. Some factors are more important while the influence of others is negligible.To find an optimum factor combination in order to maximize penetration and minimize the HAZ is not an easy task. This contribution is aimed to evaluate the influence of welding energy (E) versus the influence of current (I) and welding speed (Vw) on the penetration and HAZ volume in the autogenous Tungsten Inert Gas (TIG) welding process. For this purpose, phenomenological and empirical models are proposed. The first considers an in-house Finite Volume numerical model, and the second is based on Response Surface Method. A sensitivity analysis of the empirical model using two strategies is also performed. In addition, to determine the best-operating conditions, a multi-objective optimization problem is proposed and solved. The presented empirical models were found to provide good concordance in terms of coefficient of determination and p-value, indicating its significance. Each model (with one or more independent variables) represents detailed information about the physical process and can be used for optimization. The sensitivity analysis demonstrates that the current affects penetration and HAZ volume much stronger than the welding speed does. Physically, this is due to the fact current has linear (arc coupling) and non-linear (Joule effect and pressure gradient) influence, and the welding speed contributes linearly, modulating the heat conduction. Finally, it was demonstrated a compromise between the penetration and the HAZ volume by addressing multi-objective optimization. In this context, point C (I = 250 A; VW= 24.8 cm/min) of the Pareto curve is the optimal option for operation since it provides a lower relative HAZ volume while keeping the same penetration and higher productivity (welding speed).

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“…Other authors applied the sensitivity analysis to correlate the input process parameters with the mechanical strength [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and multi-objective optimization of tungsten inert gas (TIG) welding based on numerical simulation

Paes

Andrade

Lobato

et al. 2022

Int J Adv Manuf Technol

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Sensitivity Analysis for Welding Parameters in Dissimilar Friction Stir Welded Joints Using Response Surface Methodology

Palani

Elanchezhian²,

Ramnath³

et al. 2020

Springer Proceedings in Materials

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Experimental Investigation of Welding Parameters on Microhardness and Tensile Behaviour of Similar and Dissimilar Submerged FSWed Aluminium Alloy Joints

Palani¹,

Elanchezhian²,

Ramnath³

et al. 2020

Springer Proceedings in Materials

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Sensitivity analysis of process parameters on tensile properties in Plasma Arc Welding of AA8011-H24 aluminium alloys and Ti3Al2.5V Titanium alloys using Response Surface Methodology

Cited by 5 publications

References 28 publications

Sensitivity analysis and multi-objective optimization of tungsten inert gas (TIG) welding based on numerical simulation

Sensitivity analysis and multi-objective optimization of tungsten inert gas (TIG) welding based on numerical simulation

Sensitivity Analysis for Welding Parameters in Dissimilar Friction Stir Welded Joints Using Response Surface Methodology

Experimental Investigation of Welding Parameters on Microhardness and Tensile Behaviour of Similar and Dissimilar Submerged FSWed Aluminium Alloy Joints

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