Title :
Application of hierarchical neural networks to pattern recognition for quality control analysis in steel-industry plants
Author :
Valle, Maurizio ; Baratta, Daniela ; Caviglia, Daniele D.
Author_Institution :
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Abstract :
Our paper focuses on the classification of surface defects in flat rolled strips in steel industry. Since this work aims at the classification of samples organized in a hierarchical way it seems natural to use a hierarchical approach. We choose a hierarchical neural architecture, based on the multilayer perceptron, which, to some extent, combines classification trees with neural network approaches. We exhaustively tested the proposed architecture in the classification of surface defects in flat rolled strips on real plant data, obtaining a higher classification accuracy with respect to the state-of-the-art technologies. This approach can be generalized to many other industrial classification problems
Keywords :
flaw detection; multilayer perceptrons; neural net architecture; pattern classification; quality control; steel manufacture; classification trees; flat rolled strips; hierarchical neural architecture; hierarchical neural networks; industrial classification; multilayer perceptron; pattern recognition; quality control analysis; steel-industry plants; surface defect classification; Classification tree analysis; Costs; Manufacturing processes; Metals industry; Neural networks; Pattern analysis; Pattern recognition; Quality control; Strips; Testing;
Conference_Titel :
Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, 1996. Proceedings., International Workshop on
Conference_Location :
Venice
Print_ISBN :
0-8186-7456-3
DOI :
10.1109/NICRSP.1996.542766