Prediction of surface roughness with genetic programming

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@Article{Brezocnik:2004:JMPT,

• author = "M. Brezocnik and M. Kovacic and M. Ficko",
• title = "Prediction of surface roughness with genetic programming",
• 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 algorithms",
• 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

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