Modelling and Simulation in Engineering / 2013 / Article / Tab 8

Research Article

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

Table 8

Experimental and ANFIS results of PVD coated 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
21050.11.50.350.3500010.3500010.350001
377.50.151.50.90.8866670.8866670.886667
477.50.151.50.860.8866670.8866670.886667
5500.1510.720.7199990.7199990.719999
677.50.211.321.321.321.32
71050.1520.80.80.80.8
81050.21.51.321.321.321.32
9500.1520.70.7000010.7000010.700001
101050.1510.580.5799990.5799990.579999
1177.50.151.50.90.8866670.8866670.886667
12500.21.50.80.80.80.8
1377.50.220.910.910.910.91
1477.50.121.321.3199981.3199981.319998

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