The modelling of hardenability using mixture density networks

Abstract: In this thesis a mixture density network has been constructed to predict steel hardenability for a given alloy composition. Throughout the work hardenability is expressed in terms of jominy profiles according to the standard jominy test. A piecewise linear description of the jominy profile has been developed to solve the problem of missing data, model identification from data based on different units and measurement uncertainty. When the underlying physical processes are complex and not well understood, as the case with hardenability modelling, mixture density networks, which are an extension of neural networks, offer a strong non-linear modelling alternative. Mixture density networks model conditional probability densities, from which it is possible to determine any statistical property. Here the model output is presented in terms of expectation values along with confidence interval. This statistical output facilitates future extension of the model towards optimisation of alloy cost. A good agreement has been obtained between the experimental and the calculated data. In order to ensure the reliability of the model in service, novelty detection of the input data is performed.