Modeling of Total Decarburization of Spring Steel with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Kovacic:2014:MMP,

• author = "Miha Kovacic",
• title = "Modeling of Total Decarburization of Spring Steel with Genetic Programming",
• journal = "Materials and Manufacturing Processes",
• year = "2015",
• volume = "30",
• number = "4",
• pages = "434--443",
• keywords = "genetic algorithms, genetic programming, Decarburisation, Linear regression, Modelling, Spring steel, Surface quality",
• ISSN = "1042-6914",
• URL = "http://dx.doi.org/10.1080/10426914.2014.961477",
• DOI = "doi:10.1080/10426914.2014.961477",
• size = "39 pages",
• abstract = "Store Steel Ltd. is one of the biggest spring steel producers in Europe. Spring steel should have proper chemical composition and microstructure and should be without surface defects. Decarburisation, that is, reduction of carbon content, also influences spring steel surface quality. During regular production the data regarding rolled spring steel bars (width, reduction rate, chemical composition and total decarburisation) and the heating furnace for heating billets before rolling (heating temperature, time and oxygen content) was monitored. On the basis of the monitored data a mathematical model for the total decarburization depth was developed by genetic programming and linear regression. The average relative deviations from experimental data for the genetic programming developed and linear regression models are 18.186percent and 22.999percent, respectively. According to developed models the heating temperature was lowered and, consequently, 24.29percent lower total decarburization (t-test, p < 0.05) and also 20.65percent lower heating furnace natural gas consumption was achieved (t-test, p < 0.05).",

}

Genetic Programming entries for Miha Kovacic

Citations