Miran Brezočnik (Author), Miha Kovačič (Author), Mirko Ficko (Author)

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.

Keywords

čelno frezanje;površinska hrapavost;napoved hrapavosti;genetsko programiranje;end milling;surface roughness;prediction of surface roughness;genetic programming;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UM FS - Faculty of Mechanical Engineering
UDC: 621.914:004.89
COBISS: 9026582 Link will open in a new window
ISSN: 0924-0136
Views: 1453
Downloads: 109
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Other data

Secondary language: English
Secondary keywords: čelno frezanje;površinska hrapavost;napoved hrapavosti;genetsko programiranje;
URN: URN:SI:UM:
Pages: str. 28-36
Issue: ǂVol. ǂ157/158
Chronology: 20. dec. 2004
ID: 8718714