Application of Neural Networks to Mechanical Property Determination of Ni-base Superalloys

Abstract

In this work the application of a neural network, within a Bayesian framework, to the prediction of tensile properties has been critically assessed. Following optimisation of input parameters a series of neural networks were trained to predict tensile properties of a range of polycrystalline superalloys as a function of temperature.
Once trained the models have been subjected to several metallurgical tests in order to demonstrate that they predict the trends expected from experimental observations. Generally it is found that the models are in agreement with expected trends. In addition areas of uncertainty are highlighted through the production of large error bars, which act as warning signals.