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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Spínola C, Bonelo JM, López J, Martínez AM, López JA, Gago A (1996) Sistema automático de adquisición de medidas en láminas de acero. OMROM Electronics, revista de información: advanced automation 12:14–16
Spínola C, Bonelo JM, Martín Vázquez MJ, Gago Bohorquez A, Vizoso J (2001) Calibration of thickness measurement instruments based on twin laser sensors. Isolines bilinear look up tables. In: Proceedings of the 18th IEEE instrumentation and measurement technology conference (IMTC 2001), Budapest, Hungary, May 2001, pp 1079–1083
Spínola C, Bonelo JM, Martín-Pérez C, Vizoso J (2004) Study of thickness deviations using a laser measuring instrument in a stainless steel production line. In: Proceedings of the 21st IEEE instrumentation and measurement technology conference (IMTC 2004), Lake Como, Italy, May 2004
Bonelo JM, Spínola C, Gago A, Vizoso J (1999) Aplicación de las redes neuronales al análisis de Mapas de Espesor en líneas de proceso de acero inoxidable. In: Proceedings of the seminario anual de automática, electrónica industrial e instrumentación (SAAEI’99), Madrid, Spain, September 1999, pp 475–478
Kohonen T (1997) Self-organizing maps, 2nd edn. Springer, Berlin Heidelberg New York
Kangas J, Kohonen T (1996) Developments and applications of the self-organizing map and related algorithms. Math Comput Simulation 41(1–2):3–12
Freeman A, Skapura DM (1991) Neural networks: algorithms, applications, and programming techniques. Addison-Wesley, Reading, Massachusetts
Kohonen T, Hunniyen J, Kangas J, Laaksonen J (1992–1997) The self-organizing map program package. Laboratory of computer and information science, Helsinki University of Technology, Finland, technical report A31
Spínola C, Bonelo JM, Gago A, Vizoso JM (2000) Filtering of impulse noise from laser sensor signals in the process of non-contact thickness measurement in a stainless steel sheet production line. In: Proceedings of the 17th IEEE instrumentation and measurement technology conference (IMTC 2000), Baltimore, Maryland, May 2000, pp 1303–1307
Sun T, Neuvo Y (1994) Detail-preserving median based filters in image processing. Pattern Recognit Lett 15:341–347
Pratt WK (2001) Digital image processing, 3rd edn. Wiley, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Galvez-Fernández, C., Spínola, C., Bonelo, J.M. et al. An approach to the analysis of thickness deviations in stainless steel coils based on self-organising map neural networks. Neural Comput & Applic 13, 309–315 (2004). https://doi.org/10.1007/s00521-004-0426-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-004-0426-z