Multi-objective optimization of process parameters in TIG-MIG welded AISI 1008 steel for improved structural integrity

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8 Citations (Scopus)


This study investigates a parametric multi-objective optimization of the Tungsten Inert Gas-Metal Inert Gas (TIG-MIG) hybrid welding of AISI 1008 mild steel joints. A combined grey relational system theory and the Taguchi method was used for process optimization towards achieving a set of process parameter that maximizes both ultimate tensile strength and 0.2% yield strength for structural applications. An L-9 orthogonal array based on the Taguchi method was adopted for the experimental design matrix. Grey relational grading system was used to establish a single grade for the responses. Mathematical models for first- and second-order regressions were developed and optimum process parameter combination that optimizes the response was obtained. From the results, the gas flow rate had the most significant influence on the responses with a percentage contribution of 39.77%. Also, the second-order regression models had a higher coefficient of determination (R2) compared to the first-order regression for the two responses and, thus, represents the best fit for the process. The grey relational grade was improved by 0.0489 through process optimization. The interactive effects of process parameters and their effects on the responses are also illustrated by response surface plots. This study shows the effectiveness of the grey relational grading system in achieving a multi-objective optimization for the TIG-MIG welding process.

Original languageEnglish
Pages (from-to)3601-3615
Number of pages15
JournalInternational Journal of Advanced Manufacturing Technology
Issue number11-12
Publication statusPublished - Feb 2022


  • Grey-based Taguchi method
  • Multi-objective optimization
  • TIG-MIG hybrid welding
  • Tensile strength
  • Yield strength

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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