Genetic based approach to predict surface roughness

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Miran Brezocnik and Mirko Ficko and Miha Kovacic",
  title =        "Genetic based approach to predict surface roughness",
  booktitle =    "8th International Research/Expert Conference Trends in
                 the Development Machinery and Associated Technology",
  year =         "2004",
  pages =        "91--94",
  address =      "Neum, Bosnia and Herzegovina",
  month =        "15-19 " # sep,
  keywords =     "genetic algorithms, genetic programming, celno
                 frezanje, povrsinska hrapavost, napoved hrapavosti,
                 genetsko programiranje, end milling, surface roughness,
                 prediction of surface roughness",
  ISBN =         "9958-617-21-8",
  URL =          "",
  URL =          "",
  abstract =     "In this paper we propose genetic programming to
                 predict surface roughness in end milling. Two
                 independent data sets were obtained from measurements:
                 the training data set and the testing data set. Spindle
                 speed, feed rate, depth of cut and vibrations were used
                 as independent input variables (parameters), while
                 surface roughness was the output variable. Different
                 surface roughness models were obtained with the
                 training data set and genetic programming. The testing
                 data set was used to prove the accuracy of the best
                 model. The conclusion is that surface roughness is most
                 influenced by the feed rate, while vibrations increase
                 the prediction accuracy.",
  jaezik =       "angleski",
  notes =        "Paper number TMT04-237
                 (broken) COBISS.SI-ID: 9009686",

Genetic Programming entries for Miran Brezocnik Mirko Ficko Miha Kovacic