TOPSIS - GRA Approach to Optimize Friction Stir Welded Aluminum 6061 Pipes Parameters

Ibrahim Sabry, Dinu Thomas Thekkuden, Abdel Hamid I. Mourad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Grey relational analysis, Technique for Order of Preference by Similarity to Ideal Solution and hybrid Technique combining Order of Preference by Similarity to Ideal Solution and Grey Relational Analysis are used to optimize process parameters of friction stir welded Aluminum 6061 pipes for greater tensile strength and hardness. These statistical approaches are known for solving multi objective problems where in this case, multi objective tensile strength and hardness are optimized with respect to friction stir welding parameters such as rotational speed, travel speed and tool pin diameter. The design of experiments were designed using Taguchi-L27 orthogonal array and responses such as tensile strength and hardness are evaluated for each set of parameters. The results from three statistical approaches converged to a single optimized set of parameter - 1800 rpm rotational speed, 5 mm pin diameter and 20 mm/min travel speed.

Original languageEnglish
Title of host publication2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665418010
DOIs
Publication statusPublished - 2022
Event2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 - Dubai, United Arab Emirates
Duration: Feb 21 2022Feb 24 2022

Publication series

Name2022 Advances in Science and Engineering Technology International Conferences, ASET 2022

Conference

Conference2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
Country/TerritoryUnited Arab Emirates
CityDubai
Period2/21/222/24/22

Keywords

  • Friction stir welding
  • GRA
  • TOPSIS
  • TOPSIS-GRA
  • aluminum 6061 pipes

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Waste Management and Disposal

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