by Tiziana Segreto, Sara Karam, Jes Ramsing, Roberto Teti
Polishing processes have evolved from being a manual operation to become an automated process based on a robotized arm. Aiming for a more accurate, robust, and reliable polishing process, sensor monitoring would be viable for quality and process control. In this paper, an acoustic emission sensor monitoring system was employed for surface roughness assessment during robot assisted polishing operations on steel bars. Two feature extraction procedures were applied to the detected acoustic emission signals: statistical and wavelet packet transform in order to obtain the relevant features to be inputted in a neural network pattern recognition paradigm for the correlation between the acoustic emission signals and the measured surface roughness.