Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms

The paper proposes an approach to the design of the chemical composition of steel, which is based on neural networks and genetic algorithms and aims at achieving a desired hardenability behavior possibly matching other constraints related to the steel production. Hardenability is a mechanical feature of steel, which is extremely relevant for a wide range of steel applications and refers to the steel capability to improve its hardness following a heat treatment. In the proposed approach, a neural-network-based predictor of the so-called Jominy hardenability profile is exploited, and an optimization problem is formulated, where the optimization function allows taking into account both the desired accuracy in meeting the target Jominy profile and other constraint. The optimization is performed through genetic algorithms. Numerical results are presented and discussed, showing the efficiency of the proposed approach together with its flexibility and easy customization with respect to the user demands and production objectives.

Automated summary

The paper presents an approach to designing the chemical composition of steel using neural networks and genetic algorithms to achieve desired hardenability behavior while considering other constraints. The proposed method utilizes a neural-network-based predictor of the Jominy hardenability profile and formulates an optimization problem to meet accuracy requirements and constraints. The results demonstrate the efficiency, flexibility, and customization of the approach for user demands and production objectives.