Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of alpha and beta phase in titanium alloys

Abstract

An artificial neural network model was developed to correlate the relationship between the alloying elements (Al, V, Fe, O, and N) and heat treatment temperature (inputs) with the volume fractions of alpha and beta phases (outputs) in some alpha, near-alpha, and alpha + beta titanium alloys. The individual and combined influences of the composition and temperature on a and b phases were simulated through performing sensitivity analysis. A new method has been proposed to estimate the relative importance of the inputs on the outputs for single phase alpha-Ti, near-alpha Ti, and alpha + beta Ti alloys. The average error of the model predictions for 35 unseen test data sets is 1.546%. The estimated behavior of volume fractions of alpha and beta phases as a function of composition and temperature are in good agreement with the experimental knowledge. Justification of the results from the metallurgical interpretation has been included. (C) 2015 Elsevier B.V. All rights reserved.