Prediction of surface roughness with genetic programming

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  author =       "M. Brezocnik and M. Kovacic and M. Ficko",
  title =        "Prediction of surface roughness with genetic
  journal =      "Journal of Materials Processing Technology",
  year =         "2004",
  volume =       "157-158",
  pages =        "28--36",
  month =        "20 " # dec # " 2004",
  keywords =     "genetic algorithms, genetic programming, Manufacturing
                 systems, Surface roughness; Milling, Evolutionary
  ISSN =         "0924-0136",
  DOI =          "doi:10.1016/j.jmatprotec.2004.09.004",
  abstract =     "In this paper, we propose genetic programming to
                 predict surface roughness in end-milling. Two
                 independent data sets were obtained on the basis of
                 measurement: training data set and testing data set.
                 Spindle speed, feed rate, depth of cut, and vibrations
                 are used as independent input variables (parameters),
                 while surface roughness as dependent output variable.
                 On the basis of training data set, different models for
                 surface roughness were developed by genetic
                 programming. Accuracy of the best model was proved with
                 the testing data. It was established that the surface
                 roughness is most influenced by the feed rate, whereas
                 the vibrations increase the prediction accuracy.",
  notes =        "Originally in AMME 2000-2002 conference
                 \cite{Brezocnik:2002:AMME}. Achievements in Mechanical
                 and Materials Engineering Conference. Selected for
                 publication as full paper in the Special Issue of the
                 Journal of Materials Processing Technology (Elsevier,
                 the Netherlands)",

Genetic Programming entries for Miran Brezocnik Miha Kovacic Mirko Ficko