Using Machine Learning Models to Predict Weld Sequence giving Minimum Distortion

Jeyaganesh Devaraj, Aiman Ziout, Jaber Abu Qudeiri, Rashfa Baalfaqih, Nasmah Baalfaqh, Kanna Alahbabi, Maitha Alnaqbi, Noura Alhosan

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

Abstract

One of the most common issues affecting the performance and reliability of a welded junction is distortion. The welding sequence has been found to have a considerable impact on distortions. In this regard, weld Sequence Optimization (WSO) is useful for preventing these constraints at the early designing phase, and minimizing total capital costs in the manufacturing sectors. Welding processes are usually decided by skilled welders, and in certain circumstances, an experimental strategy may be required. In the present study, realistic experimentation, simulation software, and Artificial Neural Networks (ANN) are used for minimizing the distortion in a complex structure. Experimentation is conducted using Gas Metal Arc Welding for a dissimilar joint from Stainless Steel SS304 with Mild Steel AISI1008, and the Finite Element Model (FEM) was created using MSC Simufact Welding solver and confirmed through a variety of trials. The objectives of this paper is to develop and test a practical strategy for predicting distortion induced during welding process and WSO employing ANN model by hot-encoding. The finding revealed that the distortion is reduced by 87.7 % from the maximum distortion obtained during the welding process.

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

  • ANN Model
  • Dissimilar Metal Welding
  • GMAW Process
  • Hot Encoding
  • Weld Sequence Optimization

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

Fingerprint

Dive into the research topics of 'Using Machine Learning Models to Predict Weld Sequence giving Minimum Distortion'. Together they form a unique fingerprint.

Cite this