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Machined surface quality isone of the key indicators of a correctly conducted milling process. This paper reports on the results from numerical and statistical analysis of the condition of AZ91D Magnesium Alloy after milling using the PCD Tool.Three surface roughness indicators were of interest –average roughness (Ra), maximum height of profile (Rz) and mean width of profile elements (RSm). The surface quality, described byRa/Rz, shows negligible deteriorationat higher speeds vc.Two artificial neural networks, MLP (Multilayer Perceptron) and RBF (Radial Basis Function), modelled with Statistica package, were employed to simulate the effects that individual process variables have on the 2D surface roughness parameters. The statistical significance of the results was assessed usingthe one-way ANOVA technique. Given the successful validation of the numerical and empirical data (R2> 0.85), it may be inferred that our ANNs are an accurate predicting tool that models milling parameters ensuring that the surface is of suitable quality.The surface roughness indicatorsare generated from the corresponding technological parameters.Simulations save time, effort and costs that would be incurred by additional machining tests.
