Abstract
A study of AA2219-T351 alloy's crack behavior and fatigue propagation rate is presented in this paper. Tests were done according to ASTM standards on fracture robustness and propagation of fatigue crack rates of CT samples fused with the 2219-T351 aluminum alloys utilizing the FSW technique. The toughness and growth rate of fatigue failure can therefore be predicted using a neural network. Different numbers of neurons are inserted into the hidden layer of the ANN to see which one performs best, and then the best network is selected. A comparison is made between the outcomes of the artificial neural network and the fitting technique and then the fitted surfaces that exhibit welding behavior are exhibited. Multi-objective optimization is used to further improve welding performance, and the impact of rotation and traversing speeds on fracture toughness at which fatigue fractures grow are examined.
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