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dc.contributor.authorZhang, K-
dc.contributor.authorButler, C-
dc.contributor.authorYang, QP-
dc.contributor.authorLu, Y-
dc.identifier.citationIEEE Transactions on Instrumentation and Measurement. 46 (4): 899-902en
dc.description.abstractThis paper presents a fiber optic sensor system, artificial neural networks (fast back-propagation) are employed for the data processing. The use of the neural networks makes it possible for the sensor to be used both for surface roughness and displacement measurement at the same time. The results indicate 100% correct surface classification for ten different surfaces (different materials, different manufacturing methods, and different surface roughnesses) and displacement errors less then ±5 μm. The actual accuracy was restricted by the calibration machine. A measuring range of ±0.8 mm for the displacement measurement was achieved.en
dc.format.extent120591 bytes-
dc.subjectArtificial neural networken
dc.subjectFiber optic sensoren
dc.subjectSurface roughnessen
dc.titleA fibre optic sensor for the measurement of surface roughness and displacement using artificial neural networksen
dc.typeResearch Paperen
Appears in Collections:Advanced Manufacturing and Enterprise Engineering (AMEE)
Dept of Mechanical and Aerospace Engineering Research Papers

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