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Optimization of Gas Metal Arc Welding Process Parameters for Enhanced Tensile Strength of Mild Steel Weldments Using the Taguchi Design of Experiments and Analysis of Variance
Subject area: Science,Engineering and Technology · Area of research: Mechanical Engineering
DOI: https://doi.org/10.64388/IREV10I1-1720144
Abstract
Gas Metal Arc Welding (GMAW) is one of the most commonly used fusion joining processes for structural steel because it has the advantages of high deposition speed, high degree of automation and low operating cost. The mechanical properties of a GMAW joint can be very sensitive to the combination of process parameters chosen, however, and a random choice of process parameters cannot reliably be used for the purpose of securing the same tensile strength. The aim of this Taguchi design of experiments study was to maximize three GMAW parameters (wire feed rate, arc voltage and welding current) in bead-on-plate welds made on ST-37 mild steel rod. An L9 orthogonal array was used for planning nine tests at three levels of parameters, and the tensile strength data were analyzed by signal-to-noise (S/N) ratio and analysis of variance (ANOVA). The wire feed rate was determined as the most significant parameter (34.64%) followed by welding current (19.42%) and arc voltage (5.95%). The optimal model combination suggested (26 V, 184.33 A, 220 m/min) was very close to the experimental trial that performed best (688 MPa). The results are compared to recent (2022–2025) studies where the GMAW process was optimised using the Taguchi method and machine learning methods, which has confirmed the generality of the trends of the process sensitivities obtained with the current and wire feed, and revealed ample potential for hybrid approaches using a combination of statistical and data-driven optimisation in future research.
Keywords
Gas Metal Arc Welding, Taguchi Method, Design of Experiments, Signal-to-Noise Ratio, Analysis of Variance, Tensile Strength, Process Parameter Optimization.
How to cite this paper
@article{1720144,
author = {Hemant Dade, Prof. Chaitanya Shrivastava, Dr. Raghvendra Singh},
title = {Optimization of Gas Metal Arc Welding Process Parameters for Enhanced Tensile Strength of Mild Steel Weldments Using the Taguchi Design of Experiments and Analysis of Variance},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {2793-2801},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1720144.pdf},
abstract = {Gas Metal Arc Welding (GMAW) is one of the most commonly used fusion joining processes for structural steel because it has the advantages of high deposition speed, high degree of automation and low operating cost. The mechanical properties of a GMAW joint can be very sensitive to the combination of process parameters chosen, however, and a random choice of process parameters cannot reliably be used for the purpose of securing the same tensile strength. The aim of this Taguchi design of experiments study was to maximize three GMAW parameters (wire feed rate, arc voltage and welding current) in bead-on-plate welds made on ST-37 mild steel rod. An L9 orthogonal array was used for planning nine tests at three levels of parameters, and the tensile strength data were analyzed by signal-to-noise (S/N) ratio and analysis of variance (ANOVA). The wire feed rate was determined as the most significant parameter (34.64%) followed by welding current (19.42%) and arc voltage (5.95%). The optimal model combination suggested (26 V, 184.33 A, 220 m/min) was very close to the experimental trial that performed best (688 MPa). The results are compared to recent (2022–2025) studies where the GMAW process was optimised using the Taguchi method and machine learning methods, which has confirmed the generality of the trends of the process sensitivities obtained with the current and wire feed, and revealed ample potential for hybrid approaches using a combination of statistical and data-driven optimisation in future research.},
keywords = {Gas Metal Arc Welding, Taguchi Method, Design of Experiments, Signal-to-Noise Ratio, Analysis of Variance, Tensile Strength, Process Parameter Optimization.},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1720144}
}