Application of artificial intelligence methods to determine the optimal process parameters in resistance projection welding of steel nuts

The study employed applied computer modelling to identify the optimal process parameters for resistance projection welding using the original procedure. The influence of technological parameters (welding power, welding time, electrode pressure) on the quality of 184 welded joints produced by resistance projection welding of steel nuts and S235JR steel plates was examined using computer modelling methods, specifically a combination of machine learning and an evolutionary algorithm. A tree-based model was used to identify relationships between signals, and a genetic algorithm for multi-criteria optimisation. The prepared joints were then examined to determine the impact of the welding parameters on the microstructure, Vickers hardness, and strength of the welded joints (as assessed by pull-off testing). The superior strength of the projection welding joints was achieved through short welding times and high power. Additionally, limited welding time effectively restricted the heat-affected zone, reducing weld hardness and improving the joint's plasticity. The original modelling process enables energy consumption (welding current) to be minimised while maximising joint strength, which was the main aim of the work. Finally, the set of optimised welding parameters selected by AI was verified through sample welding and strength testing, and this was confirmed through final strength testing experiments.