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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