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Prediction of Surface Roughness in End-Milling with Multiple Regression Model | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 26, Issue 3, 2008, Pages 326-337 PDF (381.37 K) | ||
| DOI: 10.30684/etj.26.3.4 | ||
| Authors | ||
| Saad Kareem Shather; Abbas Fadhel Ibrheem | ||
| Abstract | ||
| In this Paper, we propose statistical package for social sciences (SPSS), to predict surface roughness. Two independent data sets were obtained on the basis of measurement: training data set and testing data set. Spindle speed, feed rate, and depth of cut are used as independent input variables (parameters) while surface roughness as dependent output variable. The multiple regression model by using (SPSS) could predict the surface roughness (Ra) with average percentage deviation of 7.8%, or 92.2%, accuracy from training data, and from testing data set that was not included in the multiple regression analysis with average percentage deviation of 11.95%, or accuracy of 88%, for 4-Flute end mill. | ||
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