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Artificial neural networks (ANN) were used to model the strength, ductility and hardness of multi-pass welds deposited by gas tungsten arc welding (GTAW) in plates of commercial titanium alloys. The input parameters of the ANN are the alloy composition and heat treatment conditions and its output is one of the mechanical properties of the weld metal of titanium alloys, namely ultimate tensile strength (UTS), yield strength, elongation, reduction of the area (ROA) and hardness. The titanium alloys used in the work include commercially pure titanium, alpha or near-alpha titanium, alpha-beta titanium and beta or near-beta titanium.
Artificial neural networks (ANN) were used to model the strength, ductility and hardness of multi-pass welds deposited by gas tungsten arc welding (GTAW) in plates of commercial titanium alloys. The input parameters of the ANN are the alloy composition and heat treatment conditions and its output is one of the mechanical properties of the weld metal of titanium alloys, including ultimate tensile strength (UTS), yield strength, elongation, reduction of the area (ROA) and hardness. pure titanium, alpha or near-alpha titanium, alpha-beta titanium and beta or near-beta titanium.