Cognitive decision making in multiple sensor monitoring of robot assisted polishing
Abstract
A multiple sensor monitoring system, comprising acoustic emission, strain and voltage sensors, was utilised during an experimental campaign of robot assisted polishing of steel bars for on-line evaluation of workpiece surface roughness. Two feature extraction procedures, based on conventional statistics and wavelet packet transform algorithms, were applied to the detected sensor signals in order to extract features to be fed to cognitive methods based on neural network pattern recognition paradigms seeking for correlations with the surface roughness of the polished workpiece.
