Prediction of Surface Roughness in End-Milling with Multiple Regression Model
In this Paper, we propose statistical package for social sciences (SPSS), to predictsurface roughness. Two independent data sets were obtained on the basis ofmeasurement: training data set and testing data set. Spindle speed, feed rate, anddepth of cut are used as independent input variables (parame...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2008-03-01
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| Series: | Engineering and Technology Journal |
| Online Access: | https://etj.uotechnology.edu.iq/article_26420_04e80df84b4b3d80cc05eb730e16ce84.pdf |
| Summary: | In this Paper, we propose statistical package for social sciences (SPSS), to predictsurface roughness. Two independent data sets were obtained on the basis ofmeasurement: training data set and testing data set. Spindle speed, feed rate, anddepth of cut are used as independent input variables (parameters) while surfaceroughness as dependent output variable. The multiple regression model by using(SPSS) could predict the surface roughness (Ra) with average percentage deviationof 7.8%, or 92.2%, accuracy from training data, and from testing data set that wasnot included in the multiple regression analysis with average percentage deviationof 11.95%, or accuracy of 88%, for 4-Flute end mill. |
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| ISSN: | 1681-6900 2412-0758 |