Roughness Parameters with Statistical Analysis and Modelling Using Artificial Neural Networks After Finish Milling of Magnesium Alloys with Different Edge Helix Angle Tools

Abstract

The paper presents the results of a study investigating the roughness parameters Rq, Rt, Rv, and Rp of finished-milled magnesium alloys AZ91D and AZ31B. Carbide end mills with varying edge helix angles were used in the study. Statistical analysis was additionally performed for selected machining conditions. In addition, modelling of selected roughness parameters on the end face for the AZ91D alloy was carried out using artificial neural networks. Results have shown that the tool with λs = 20° is more suitable for the finish milling of magnesium alloys because its use leads to a significant reduction in surface roughness parameters with increased cutting speed. Increased feed per tooth leads to increased surface roughness parameters. Both radial and axial depth of cut has an insignificant effect on surface roughness parameters. It has been proven that finish milling is an effective finishing treatment for magnesium alloys. In addition, it was shown that artificial neural networks are a good tool for the prediction of selected surface roughness parameters after finishing milling of the magnesium alloy AZ91D.