Title :
Neural network classification of metal surface properties using a dynamic touch sensor
Author :
Brenner, Dean ; Principe, Jose C. ; Doty, Keith L.
Author_Institution :
Dept. of Electr. Eng., Florida Univ., Gainesville, FL, USA
Abstract :
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
Keywords :
classification; computerised pattern recognition; electric sensing devices; learning systems; machining; mechanical engineering computing; metals; neural nets; surface topography measurement; accuracy; automated characterization; backpropagation; dynamic touch sensor; feedforward one-hidden-layer neural network; machining; metal surface properties; preprocessing; signal classification; spectral features; Backpropagation; Force sensors; Machining; Needles; Neural networks; Rough surfaces; Sensor phenomena and characterization; Surface roughness; Surface texture; Tactile sensors;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155174
