NDLI: An approach to the analysis of thickness deviations in stainless steel coils based on self-organising map neural networks

Content Provider	Springer Nature Link
Author	Galvez Fernández, Carlos Spíla, Carlos Bonelo, José M. Tapia, F. J. Martín Vizoso, Julio
Copyright Year	2004
Abstract	The aim of this work is to classify the sections of coils produced on a cool rolling mill that have an irregular thickness pattern, in order to achieve a homogeneous thickness in each coil. In order to do this investigation, we have employed a self-organising map (SOM) of neural networks, a new segmentation and clustering algorithm, filters to reduce the noise and, finally, a classification calculated from the difference between the value of each sample taken and the average of them all. We have introduced an alternative approach, with improvements in the segmentation and clustering steps, which has been successfully applied in an industrial production line. Some of our limitations and future areas for investigation are also included.
Starting Page	309
Ending Page	315
Page Count	7
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	13
Issue Number	4
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2004-11-06
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural networks applications Self-organising maps Data series analysis and clustering
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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An approach to the analysis of thickness deviations in stainless steel coils based on self-organising map neural networks