Neural networks for web-process inspection

摘要

This paper examines two issues imposed upon any industrial inspection system using a neural network the feature set which the sensory system must provide and the accuracy of neural network based-inspection. The context is web-process inspection which requires rapid examination of vast amounts of data for on-line detection of faults in the sheet material. Feature vectors with nine or 17 dimensions, created by a simulated segmented photodetector using measurement of the angular distribution over a 25° cone angle of the scattering were evaluated for inspection of CrO{sub}2 coated sheet steel samples. The scattered coherent light from the surface of the material being processed could be directly conditioned by a photodetector 80 as to produce this small set of features which are then examined by a neural network trained to find and categorize unsatisfactory surface conditions. Details are presented to show how a modified feature set was developed and tested after an examination of feature space. This new, smaller set proved to be more accurate than the larger set. Classification by fault or no fault categorized 133 samples correctly out of 135, while there were seven errors in one attempt at classification into the various common surface faults out of the same number of test samples and nine in another. It is shown that a bit of insight in feature selection can improve the capability of the network to recognize faults.