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Development of an Artificial Neural Network Model to Predict the Properties of Alpha and Near Alpha Titanium Alloys as a Function of Their Composition

P. S. Noori Banu, S. Devaki Rani

In the current study, an artificial neural network (ANN) model was developed to predict the properties of alpha and near alpha titanium alloys as a function of their composition. The input parameters of the ANN model consist of composition and heat treatment parameters of alpha and near alpha titanium alloys. Property parameters such as tensile strength, yield strength, percentage elongation, reduction in area, Charpy impact, rock well hardness, modulus of elasticity-tensional, modulus of elasticity-torsional, electrical resistivity and thermal expansion were included as the output parameters. The experimental datasets of thirteen (seven alpha and six near alpha) alloys were used for training. The predictability of each property using this model has been evaluated in terms of accuracy, correlation coefficient (R) and average absolute relative error (ARRE). Our study was able to predict the properties of titanium alloys with good accuracy. The results obtained were compared to the experimental data and are found to be consistent. The developed model can be used as a working as well as guiding tool for the design and development of alpha and near alpha alloys. Further, temperature-dependent effects on properties were evaluated using linear regression models.