Optimization of Bead Geometry during Tungsten Inert Gas Welding Using Grey Relational and Finite Element Analysis

Abstract

Mild steel welded products are widely used for their excellent ductility. Tungsten inert gas (TIG) welding is a high-quality, pollution-free welding process suitable for a base part thickness greater than 3 mm. Fabricating mild steel products with an optimized welding process, material properties, and parameters is important to achieve better weld quality and minimum stresses/distortion. This study uses the finite element method to analyze the temperature and thermal stress fields during TIG welding for optimum bead geometry. The bead geometry was optimized using grey relational analysis by considering the flow rate, welding current, and gap distance. The welding current was the most influential factor affecting the performance measures, followed by the gas flow rate. The effect of welding parameters, such as welding voltage, efficiency, and speed on the temperature field and thermal stress were also numerically investigated. The maximum temperature and thermal stress induced in the weld part were 2083.63 °C and 424 MPa, respectively, for the given heat flux of 0.62 × 106 W/m2. Results showed that the temperature increases with the voltage and efficiency of the weld joint but decreases with an increase in welding speed.

From Uncertainty to Precision: Advancing Industrial Rework Rate Analysis with Fuzzy Logic

“…A study conducted by (dos Santos et al, 2017) employed fuzzy logic to evaluate the relationship between assembly process quality and rework rate in an electronic component production line. Similarly, (Abdel-Tawab & Noor, 1999) explored the application of a fuzzy model to analyze the influence of temperature variations on rework rate in welding processes (Hanif et al, 2023). Furthermore, the integration of fuzzy models with correlation analysis has enabled deeper insights into causal relationships and underlying patterns in the rework rate.…”

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

“…FEM-based modelling can investigate how process parameters affect the dimensional accuracy of the component and optimize process parameters [12,13]. Hanif et al [14] employed FEM to study the temperature and thermal stress field in the TIG welding process and applied the GRA method to obtain the optimal weld bead geometry by comprehensively considering various factors such as welding current, shielding gas flow rate, and standoff distance on the weld bead.…”