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<p>This study investigates the TIG welding of AISI 316L austenitic stainless steel by systematically varying key process parameters—welding current, welding speed, and gas flow rate—to evaluate their effects on the mechanical performance of welded joints. The output responses considered were ultimate tensile strength (UTS) and impact strength (IS), with a “higher-the-better” criterion applied for UTS. Analysis of variance (ANOVA) and Box–Behnken response surface methodology (RSM) were employed to model the influence of input parameters and their interactions on the responses. Main, interaction, and contour plots were generated to visualize parameter effects and identify feasible operating regions. Parametric optimization was performed using Teaching–Learning-Based Optimization (TLBO) and the desirability function method (DFA) to determine conditions maximizing UTS and IS. The study revealed that gas flow rate significantly affects UTS, whereas welding current predominantly influences IS. Optimum conditions were found to be 100 A, 2&#xa0;mm/s, 20 L/min for UTS (567&#xa0;MPa) and 115 A, 2&#xa0;mm/s, 20 L’min for IS (46.37&#xa0;J). Confirmatory experiments validated the predicted improvements in mechanical performance, demonstrating the effectiveness of the modeling and optimization approach for enhancing TIG-welded 316L stainless steel joints.</p> Graphical Abstract <p> Graphical representation of optimization of TIG welding parameters using RSM, TLBO, and DFA</p> <p></p>

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Utilization of the desirability function approach and teaching learning based optimization strategy to improve tensile and impact properties on tig welded 316L austenitic steel weldments

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Nabendu Ghosh

摘要

This study investigates the TIG welding of AISI 316L austenitic stainless steel by systematically varying key process parameters—welding current, welding speed, and gas flow rate—to evaluate their effects on the mechanical performance of welded joints. The output responses considered were ultimate tensile strength (UTS) and impact strength (IS), with a “higher-the-better” criterion applied for UTS. Analysis of variance (ANOVA) and Box–Behnken response surface methodology (RSM) were employed to model the influence of input parameters and their interactions on the responses. Main, interaction, and contour plots were generated to visualize parameter effects and identify feasible operating regions. Parametric optimization was performed using Teaching–Learning-Based Optimization (TLBO) and the desirability function method (DFA) to determine conditions maximizing UTS and IS. The study revealed that gas flow rate significantly affects UTS, whereas welding current predominantly influences IS. Optimum conditions were found to be 100 A, 2 mm/s, 20 L/min for UTS (567 MPa) and 115 A, 2 mm/s, 20 L’min for IS (46.37 J). Confirmatory experiments validated the predicted improvements in mechanical performance, demonstrating the effectiveness of the modeling and optimization approach for enhancing TIG-welded 316L stainless steel joints.

Graphical Abstract

Graphical representation of optimization of TIG welding parameters using RSM, TLBO, and DFA