Grey‐based taguchi multiobjective optimization and artificial intelligence‐based prediction of dissimilar gas metal arc welding process performance

Jeyaganesh Devaraj, Aiman Ziout, Jaber E. Abu Qudeiri

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above‐mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey‐based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro‐fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as train-ing and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

Original languageEnglish
Article number1858
JournalMetals
Volume11
Issue number11
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Adaptive neuro‐fuzzy inference system (ANFIS)
  • Artificial neural network (ANN)
  • Dissimilar metal welding
  • Gas metal arc welding
  • Grey‐based Taguchi optimization

ASJC Scopus subject areas

  • Materials Science(all)
  • Metals and Alloys

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