Estimation of the Interaction Parameters of Liquid Fe using Neural Network Computation

Abstract

The estimation of interaction parameters in liquid iron is strongly demanded due to the difficulty of their measurements and its time consuming for enormous combinations of target solute elements in liquid iron. Therefore, several estimation models have been developed so far. In this study, the interaction parameters between metal elements and/or metalloid elements in liquid Fe are estimated by neural network computation in order to improve the estimation accuracy. The input parameters used in the neural network computation are assessed by lateral inhibition learning. The estimation results by nerural network computation with the assessed parameters reasonably agree with the recommended values in the literature.