Prediction of Surface Roughness When End Milling <i>Ti6Al4V</i> Alloy Using Adaptive Neurofuzzy Inference System

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td align="left">Ultimate tensile strength MPa</td><td align="center">Yield strength MPa</td><td align="center">Rockwell hardness HRC</td><td align="center">Modulus of elasticity GPa </td><td align="center">Poisson's ratio</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td align="left">950</td><td align="center">880</td><td align="center">36</td><td align="center">113.8</td><td align="center">0.342</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Mechanical properties of <i>Ti6Al4V</i> alloy.

Modelling and Simulation in Engineering

tab4

Table 4

Table 4: Prediction of Surface Roughness When End Milling <i>Ti6Al4V</i> Alloy Using Adaptive Neurofuzzy Inference System 

Modelling and Simulation in Engineering

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

Table 4