Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

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 training 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.

Study of the Microstructure and Mechanical Property Relationships of Gas Metal Arc Welded Dissimilar Protection 600T, DP450 and S275JR Steel Joints

“…is crucial in industries for their safe and efficient utilization [6]. In research conducted for this purpose, it has been determined that gas metal arc welding is one of the most effective techniques in joining dissimilar metal materials [7][8][9][10][11]. Gas metal arc welding of dissimilar metal materials typically involves joining at least two metals or alloys with different chemical compositions, melting temperatures, and thermal expansion properties [12].…”

Simultaneous optimization of weld bead geometry and weld strength during gas tungsten arc welding of Inconel 825 strips using desirability function coupled with grey relational analysis (DF-GRA)

“…penetration and HAZ area [23]. Devaraj et al (2021) developed a Taguchi-GRA approach to optimize dissimilar GMAW parameters for optimal dilution percentage and weld bead geometry [24].…”