PREDICT ARC LENGTH

The quality of Tungsten Inert Gas (TIG) welding depends significantly on the appropriate selection of process parameters such as welding current, voltage, and welding speed. Improper parameter combinations often lead to defects in weld bead geometry, reduced penetration, and an enlarged heat-affected zone. To achieve a stable arc and improved weld integrity, it becomes necessary to determine the optimal combination of these parameters. Hence, the aim of this work is to develop and apply an Ant Colony Optimization (ACO) algorithm to minimize the arc length of TIG welds using MATLAB 2024. The research methodology involved formulating an objective function that relates the arc length to the key process variables—current (A), voltage (V), and welding speed (F). The ACO metaheuristic technique was employed to simulate the welding process, with each ant representing a possible parameter combination. The pheromone updating and heuristic information were used to guide the search towards optimal solutions, and the developed algorithm was implemented and coded in MATLAB 2024. The results of the optimization revealed that the ACO algorithm successfully determined the optimal process parameters of 100 A current, 19.27 V voltage, and 0.145 m/min welding speed, producing a minimum arc length of approximately 0 mm after correction. This indicated a highly concentrated and stable arc. The predicted results were compared with the literature model and found to be in good agreement, confirming the accuracy and robustness of the developed algorithm. The findings demonstrate that ACO is an effective metaheuristic technique for intelligent optimization of TIG welding parameters, capable of enhancing weld quality through efficient parameter selection