Prediction of the Bending Capability of Rolled Metal Sheet by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Kovacic:2007:MMP,
  author =       "Miha Kovacic and Peter Uratnik and Miran Brezocnik and 
                 Radomir Turk",
  title =        "Prediction of the Bending Capability of Rolled Metal
                 Sheet by Genetic Programming",
  journal =      "Materials and Manufacturing Processes",
  year =         "2007",
  volume =       "22",
  number =       "5",
  pages =        "634--640",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Bending
                 capability, Metal sheet, Rolling, Titanzinc",
  ISSN =         "1532-2475",
  DOI =          "doi:10.1080/10426910701323326",
  abstract =     "The paper proposes genetic programming (GP) to predict
                 the bending capability of rolled titanzinc metal sheet.
                 In this study ZnTiCu alloy with 0.1percent Cu and
                 0.1percent Ti was used for production of metal sheet.
                 Three groups of independent input variables were
                 measured: (1) chemical composition of the ZnTiCu alloy
                 during casting (percentage of Cu, Ti, and Fe), (2)
                 parameters of hot rolling (temperature of ingot before
                 rolling, time of rolling, temperature of plate after
                 rolling, time of cooling), and (3) parameters of cold
                 rolling (temperature of plate before rolling,
                 temperature of sheet after rolling). Therefore, nine
                 input variables (parameters) influence the bending
                 capability of the sheet metal. On the basis of the
                 experimental data, several models for prediction of the
                 bending capability of titanzinc metal sheet were
                 developed by the simulated evolution. The influence of
                 individual input variables on bending capability was
                 also studied. The most accurate model was verified with
                 an independent testing data set. The results showed
                 that GP is a powerful tool for predicting the bending
                 capability of metal sheet.",
}

Genetic Programming entries for Miha Kovacic Peter Uratnik Miran Brezocnik Radomir Turk

Citations