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.
Original language | English |
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Pages (from-to) | 333-338 |
Number of pages | 6 |
Journal | Procedia CIRP |
Volume | 33 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | 9th CIRP International Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2014 - Capri, Italy Duration: 23 Jul 2014 → 25 Jul 2014 |
Keywords
- Feature extraction
- Neural networks
- Polishing
- Sensor fusion
- Sensor monitoring
- Surface roughness