An artificial intelligence approach in designing new materials

Purpose: The paper presents the computer aided method of chemical composition designing the metallic materials with a required property. Design/methodology/approach: The purpose has been achieved in two stages. In the first stage a neural network model for calculating the Jominy curve on the basis of the chemical composition has been worked out. This model made possible to prepare, in the second stage, a representative set of data and to work out the neural classifier that would aid the selection of steel grade with the required hardenability. Findings: Obtained results show that AI tools used are effective and very useful in designing new metallic materials. Research limitations/implications: The presented models may be used in the ranges of mass concentrations of alloying elements presented in the paper. The methodology presented in the paper makes it possible to add new grades of steel to the models. Practical implications: The worked out models may be used in computer systems of steel selection and designing for the heat-treated machine parts. Originality/value: The use of the artificial intelligence method, particularly the neural networks as a tool for designing the chemical composition of steels with the required properties.