Designing the Chemical Composition of Steel with Required Hardenability Using Computational Methods

Abstract

This paper introduces an innovative approach that enables the automated and precise prediction of steel’s chemical composition based on the desired Jominy curve. The microstructure, and in fact the presence of martensite, is decisive for the hardness of the steel, so the study considered the occurrence of this phase at particular distances from the quenched end of the Jominy sample. Steels for quenching and tempering and case hardening were investigated. With the representative collected dataset of hardness values from the quenched end of the Jominy specimen, microstructure and chemical composition of steels, the complex regression model was made using supervised artificial neural networks. The balance between cost and required hardenability can be achieved through optimizing the chemical composition of steel. This model of designing steel with required hardenability can be of great benefit in the mechanical engineering and manufacturing industry. The model is verified experimentally.