Multidimensional signal processing and modeling with neural networks in metal machining: Cutting forces, vibrations, and surface roughness

Neural networks are a soft computing technique with wide application in signal processing as well as system and process modeling. In the present study, multilayer perceptron (MLP) neural networks were employed to process multidimensional signals generated in metal machining operations (including three-dimensional cutting force signals and three-dimensional cutting vibration signals) and to establish a model for predicting the machined surface roughness. This paper describes in detail our methods of multidimensional signal processing and modeling with MLP neural networks. The MLP neural network model developed in the present study fills an important research gap by taking into account the critical effect of tool-edge radius in machining. As compared to regression models, the MLP neural network model developed in the present study has significantly higher accuracy in predicting the machined surface roughness.