Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System

Table 9

Experimental and ANFIS results of uncoated cutting tool.

No.	Cutting speed (m/min)	Feed rate (mm/tooth)	Depth of cut (mm)	Surface roughness (µm)	Model A	Model B	Model C
1	77.5	0.1	1	0.55	0.55	0.55	0.55
2	105	0.1	1.5	0.666	0.666	0.666	0.666
3	77.5	0.15	1.5	0.783	0.768667	0.768667	0.768667
4	77.5	0.15	1.5	0.85	0.768667	0.768667	0.768667
5	50	0.15	1	0.565	0.565	0.564999	0.565
6	77.5	0.2	1	1.426	1.426001	1.426	1.426
7	105	0.15	2	0.767	0.767	0.767	0.767
8	105	0.2	1.5	1.912	1.912	1.911999	1.912
9	50	0.15	2	1.173	1.172999	1.173	1.173
10	105	0.15	1	0.84	0.84	0.84	0.84
11	77.5	0.15	1.5	0.673	0.768667	0.768667	0.768667
12	50	0.2	1.5	1.444	1.443999	1.443999	1.443999
13	77.5	0.2	2	1.66	1.660001	1.660001	1.660001
14	77.5	0.1	2	0.856	0.856	0.856	0.856