Open AccessApplication of neural networks in steels' chemical composition design
Author(s) -
L. A. Dobrzaski,
W. Sitek
Publication year - 2003
Publication title -
journal of the brazilian society of mechanical sciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.431
H-Index - 40
eISSN - 1806-3691
pISSN - 1678-5878
DOI - 10.1590/s1678-58782003000200012
Subject(s) - hardenability , artificial neural network , computer science , composition (language) , domain (mathematical analysis) , point (geometry) , artificial intelligence , mechanical engineering , engineering , alloy , materials science , mathematics , metallurgy , mathematical analysis , linguistics , philosophy , geometry
Designing of the chemical composition of the steel heats having the demanded properties, e.g. the defined shape of the hardenability curve, is the crucial task from the manufacturing point of view. Rapid development of computer science and technology as well as of modern computer tools, artificial intelligence among them, prompts their increasingly common use in different domains of science and technology. There is a great interest in these methods, which seems justified, since they can be applied both to solving novel problems and to dealing with the ones considered classical. For a couple of years, such trends have been present also in the domain of materials engineering. Contemporary software tools, especially methods of artificial intelligence, make it possible to develop the method, presented in the paper, of designing of the chemical composition of constructional alloy steels, which still are one of the basic groups of metallic engineering materials. It lets the designer abandon the classical approach to the material selection according to which one of the catalogued materials has to be selected. The paper presents the method of designing of the chemical composition basing on the known and the required shape of the hardenability curve with the use of the dedicated neural networks models