Modelling and Simulation in Engineering / 2013 / Article / Tab 9

Research Article

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 AModel BModel C

177.50.110.550.550.550.55
21050.11.50.6660.6660.6660.666
377.50.151.50.7830.7686670.7686670.768667
477.50.151.50.850.7686670.7686670.768667
5500.1510.5650.5650.5649990.565
677.50.211.4261.4260011.4261.426
71050.1520.7670.7670.7670.767
81050.21.51.9121.9121.9119991.912
9500.1521.1731.1729991.1731.173
101050.1510.840.840.840.84
1177.50.151.50.6730.7686670.7686670.768667
12500.21.51.4441.4439991.4439991.443999
1377.50.221.661.6600011.6600011.660001
1477.50.120.8560.8560.8560.856

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