Modeling of chemical elements and mechanical property for TC11 titanium alloy based on the artificial neural network

Based on a large amount of experimental data, the relationship model of chemical elements and mechanical property for TC11 titanium alloy has been developed using artificial neural network. The input parameters of this model were 9 kinds of elements, including Al, Mo, Zr, Si, Fe, C, O, N and H. The mechanical properties were used as output parameters, including ultimate tensile strength, yield strength, elongation and reduction of area. The prediction capability of the established model was tested by the unseen data sample. Additionally, the effect of chemical elements (Al, Mo, Zr and C) on the mechanical property was studied using the present model. It is found that the relative errors between predicted and experimental values all within 10%, indicating that the neural network model possesses excellent prediction capability. With the help of the trained ANN model, the nonlinear relationship of chemical elements and mechanical property can also be clearly presented.