A nested-ANN prediction model for surface roughness considering the effects of cutting forces and tool vibrations

This paper demonstrates a nested-ANN (Artificial Neural Network) model predicting surface roughness (Ra). The special ANN includes enclosed-ANNs and an output-ANN. The enclosed-ANN models use cutting parameters as inputs to predict the values of cutting forces and tool vibrations respectively, and then forward all outputs to the output-ANN model. Subsequently, the output-ANN adopts the forward values and cutting parameters as inputs to predict Ra. To verify the effectiveness of the nested-ANN model, it is compared with mathematical and statistical models based on conventional ANN and RSM (Response Surface Methodology) using the same experimental data. The results show that the nested-ANN uses less input variables to obtain superior prediction accuracy than other models. Additionally, the statistical analyses show that Ra is mostly affected by the feed rate and has a signification correlation with the feed rate, the cutting force in both radial and tangential directions as well as the tool vibrations.