Modeling of Total Decarburization of Spring Steel with Genetic Programming

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  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 =          "",
  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