Research Article
Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System
Table 2
Experimental results used in training phase.
| No. | Cutting speed (m/min) | Feed rate (mm/rev) | Depth of cut (mm) | Surface roughness (µm) | PVD | Uncoated |
| 1 | 77.5 | 0.1 | 1 | 0.55 | 0.55 | 2 | 105 | 0.1 | 1.5 | 0.35 | 0.666 | 3 | 77.5 | 0.15 | 1.5 | 0.9 | 0.783 | 4 | 77.5 | 0.15 | 1.5 | 0.86 | 0.85 | 5 | 50 | 0.15 | 1 | 0.72 | 0.565 | 6 | 77.5 | 0.2 | 1 | 1.32 | 1.426 | 7 | 105 | 0.15 | 2 | 0.8 | 0.767 | 8 | 105 | 0.2 | 1.5 | 1.32 | 1.912 | 9 | 50 | 0.15 | 2 | 0.7 | 1.173 | 10 | 105 | 0.15 | 1 | 0.58 | 0.84 | 11 | 77.5 | 0.15 | 1.5 | 0.9 | 0.673 | 12 | 50 | 0.2 | 1.5 | 0.8 | 1.444 | 13 | 77.5 | 0.2 | 2 | 0.91 | 1.66 | 14 | 77.5 | 0.1 | 2 | 1.32 | 0.856 |
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