Prediction of surface roughness with genetic programming
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- @Article{Brezocnik:2004:JMPT,
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author = "M. Brezocnik and M. Kovacic and M. Ficko",
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title = "Prediction of surface roughness with genetic
programming",
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journal = "Journal of Materials Processing Technology",
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year = "2004",
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volume = "157-158",
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pages = "28--36",
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month = "20 " # dec # " 2004",
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keywords = "genetic algorithms, genetic programming, Manufacturing
systems, Surface roughness; Milling, Evolutionary
algorithms",
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ISSN = "0924-0136",
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DOI = "doi:10.1016/j.jmatprotec.2004.09.004",
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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.",
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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
Citations