Paper Info
Title
Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms
Abstract
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.
Year
DOI
Venue
2021
10.1007/s00521-021-06242-w
Neural Computing and Applications
Keywords
DocType
Volume
Neural networks, Genetic algorithms, Optimization, Material properties prediction, Steel, Jominy profile
Journal
33
Issue
ISSN
Citations 
23
0941-0643
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Marco Vannucci19415.60
Valentina Colla215929.50