Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys

摘要

Friction Stir Welding (FSW), as a solid state welding process, seems to be one of the most promising techniques for joining titanium alloys avoiding a large number of difficulties arising from the use of traditional fusion welding processes. In order to pursue cost savings and a time efficient design, the development of numerical simulations of the process can represent a valid choice for engineers. In the paper an artificial neural network was properly trained and linked to an existing 3D FEM model for the FSW of Ti-6A1-4V titanium alloy, with the aim to predict both the microhardness values and the microstructure of the welded butt joints at the varying of the main process parameters. A good agreement was found between experimental values and calculated results.