Neural network classification of metal surface properties using a dynamic touch sensor
Discusses an application of neural networks to classify signals produced by a dynamic touch sensor, and achieve automated characterization of metal surfaces during machining. Data are first preprocessed and the spectral features are input to a feedforward one-hidden-layer neural network, trained with backpropagation. The classification accuracy was over 90% for most of the surfaces. The authors discuss the experimental setup, the preprocessing, and a critical view of the classification results.<<ETX>>