Experimental investigation and optimization of quality characteristics during friction stir welding of Al- and Zn-based magnesium alloy using artificial intelligence

Abstract

Friction stir welding is successfully used to weld different wrought magnesium alloys. This work investigated the mechanical and microstructural behavior of the friction stir-welded AZ31B magnesium alloy. The experiments were conducted as per experimental runs designed by response surface methodology. An artificial neural network model was developed to produce a relationship between process variables (tool rotational speed, welding speed, and tool shoulder diameter) and characteristics of the friction stir-welded joints (tensile strength, percentage elongation, impact strength, microhardness, and grain size). The acceptable range of statistical parameters validated the adequacy of the model. The multi-objective optimization technique, genetic algorithm was used to obtain a set of Pareto optimal solutions. The best-compromised optimum solution for maximum tensile strength (164.2 MPa), percentage elongation (8%) impact strength (3.5 J), microhardness (85 Hv), and minimum grain size (13.1 μm) was validated by confirmation test with <3 percent absolute error percentage. The fractographical analysis has been performed and dimples and torn edges observed in fracture zones.