E. EBOJOH

The aim of this study is to optimize the impact energy of Tungsten Inert Gas (TIG) mild steel welds by identifying the most effective combination of welding parameters current, voltage, and gas flow rate to achieve the best mechanical performance. The specific objectives include developing a mathematical model to describe the relationship between these parameters and impact energy, applying a metaheuristic algorithm to determine the optimal settings, and validating the optimized results against existing experimental data. This research seeks to address the limitations of traditional trial-and-error and local statistical optimization techniques, which often fail to locate the true global optimum. The study employed a hybrid computational optimization approach that combines Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO). RSM was first used to develop a second-order regression model of impact energy based on existing experimental data from TIG welding of mild steel. This model served as the objective function for the PSO algorithm, which was implemented in MATLAB. The PSO algorithm iteratively adjusted welding parameters to maximize the predicted impact energy, thereby exploring the solution space beyond the limits of conventional statistical methods. The results showed that the optimal welding parameters were 192.73 A (current), 19.12 V (voltage), and 20.23 L/min (gas flow rate), corresponding to a maximum predicted impact energy of 118.52 J. This value slightly exceeded the best experimental result of 116.48 J reported in literature, confirming the effectiveness and accuracy of the hybrid RSM–PSO framework. The optimized results not only align closely with existing research trends but also demonstrate that integrating metaheuristic algorithms into welding parameter selection can enhance weld toughness, minimize experimental effort, and improve process reliability

The continuous advancement in manufacturing and fabrication industries has led to the
development of numerous welding techniques designed to achieve high quality joints with superior mechanical performance. Among these, Tungsten Inert Gas (TIG) welding, also referred to as Gas Tungsten Arc Welding (GTAW), stands out for its precision, versatility, and ability to produce defect free welds on a variety of metals. The process is particularly effective for mild steel, which is extensively used in structural, automotive, and construction applications due to its good formability, weldability, and moderate strength (Singh & Sharma, 2020). However, achieving optimal weld quality in TIG welding is challenging because the mechanical properties of the welded joint depend on several interacting parameters, including welding current, welding voltage, gas flow rate, and welding speed. These parameters collectively determine the heat input, cooling rate, and solidification behaviour of the weld pool, which in turn influence responses such as hardness, tensile strength, yield strength, elongation, shear strength, and impact energy (Kumar & Yadav, 2018). Selecting the wrong combination of these parameters may result in weld defects, reduced mechanical strength, and inconsistent joint performance.

The application of metaheuristic approach in optimising some welding parameters in TIG welding of mild steel is presented in this work. The study aims to optimise arc efficiency (AE) and thermal efficiency (TE) of the TIG welding process by applying metaheuristic optimisation techniques (MTOs), specifically the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The investigation focuses on identifying the optimal combination of welding parameters current, voltage, and gas flow rate that maximise process efficiency while maintaining physical validity and conformity with established TIG welding standards. The research methodology involved implementing mathematical models of arc and thermal efficiency based on the Goldak double-ellipsoidal heat source model. These models were coded and executed using MATLAB R2024b, where GA and PSO algorithms were used independently to optimise the input parameters within defined physical ranges obtained from validated literature. Simulation runs recorded iteration-wise outputs for each parameter, allowing convergence analysis and comparative assessment between both algorithms in terms of solution quality and computational performance. The results revealed that for arc efficiency, GA achieved optimum values at 75.59A, 14.80V, and
11.27L/min, yielding an AE of 0.81, while PSO attained optimal conditions at 63.16 A, 15.57 V, and 6.97 L/min with an AE of 0.97. For thermal efficiency, GA recorded optimum values at 67.26A, 17.21V, and 13.69L/min giving TE of 0.89, whereas PSO produced 92.09A, 18.82V, and 8.47L/min resulting in TE of 0.99. The optimised efficiency values were validated with literature and found to be in close agreement with the established efficiency range for TIG welding (0.36– 0.90), confirming the reliability of the metaheuristic approach.