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Proceedings Paper

Neural-network-based inspection of machined surfaces using laser scattering
Author(s): Sheldon Gruber; Leda Villalobos
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Paper Abstract

Features &e extrzted from the angular specrwn of light scattered fnxn the sur(e of machined surfxes which are then used as inputs to a hrarcha1 neural net. The net is "U1nCd by aselected training set of feures from surfaces whose quality has already been independently establishet These samplessicrepeatedlypresented to the sensors and the network makes a deCISIOn about the surface roughness which is then compared to the rrect answa, and the TOf used to modify the conntion weights. Following this training pcziod, the net is be able to identify the quality of new surfes presented to IL Indeed, it is able to ck, so even in the presence of noise which is likely to be caused by poor illumination. Results, using this laser scattering technique, from a set of prepared sirfaces are dixussed with regard to fusion of different features in der to obtain an adequate measure of surfe roughness ung the harchkal newal nwor

Paper Details

Date Published: 1 August 1990
PDF: 10 pages
Proc. SPIE 1265, Industrial Inspection II, (1 August 1990); doi: 10.1117/12.20236
Show Author Affiliations
Sheldon Gruber, Case Western Reserve Univ. (United States)
Leda Villalobos, Case Western Reserve Univ. (United States)


Published in SPIE Proceedings Vol. 1265:
Industrial Inspection II
Donald W. Braggins, Editor(s)

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