Evolutionary approach to manufacturing control

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

@InProceedings{Kovacic:2004:FAIM,
  author =       "Miha Kovacic and Joze Balic and Miran Brezocnik",
  title =        "Evolutionary approach to manufacturing control",
  booktitle =    "Proceedings of the 14th International conference on
                 flexible automation \& intelligent manufacturing",
  year =         "2004",
  editor =       "Lihui Wang and Jeff Xi and William G. Sullivan and 
                 Munir Ahmad",
  pages =        "915--921",
  address =      "Toronto, Canada",
  month =        jul # " 12-14",
  organisation = "Ryerson University \& National Research Council
                 Canada",
  publisher =    "NRC Research Press",
  keywords =     "genetic algorithms, genetic programming, proizvodni
                 sistemi, inteligentni obdelovalni sistemi, genetsko
                 programiranje, umetna inteligenca, evolucijske metode,
                 manufacturing systems, intelligent manufacturing
                 system, artificial intelligence, simulation ,
                 evolutionary methods",
  ISBN =         "0-662-37218-2 (Vol.1)",
  ISBN =         "0-662-37241-7 (Vol.2)",
  ISBN =         "0-662-37217-4",
  abstract =     "The paper presents the use of evolutionary method for
                 improving efficiency of the production process. The
                 purpose of research was to select the parameters of
                 turning the monel alloy in order to increase
                 productivity with required tool wearing resistance and
                 surface quality. Three independent variables, cutting
                 speed, feed rate, and cutting depth were monitored
                 between cutting process. Those variables influence the
                 dependent variables, i.e., tool life and surface
                 roughness. On the basis of experimental data set,
                 different models for tool life and surface roughness
                 were developed by genetic programming method. The two
                 models for tool life and surface roughness predict if
                 the specified tool life and surface roughness are
                 reached. The limit for high quality machining of the
                 surface was the quality class N6 (Ra=0.8 m), whereas
                 for tool life the time of 13 minutes and more was
                 expected. After process modelling the parameter
                 selection plan was developed according to predicted
                 tool life and surface roughness. The proposed concept
                 robustly and simply ensures monitoring and optimization
                 of the turning process. The results of researches were
                 transferred into practice.",
  notes =        "

                 http://www.faim2008.org/faimhistory.html",
}

Genetic Programming entries for Miha Kovacic Joze Balic Miran Brezocnik

Citations