Title: Development of neural network-based models to predict mechanical properties of hot dip galvanised steel coils
Authors: Ana Gonzalez-Marcos, Fernando Alba-Elias, Manuel Castejon-Limas, Joaquin Ordieres-Mere
Addresses: Dept. Ingenieria Mecanica, Universidad de La Rioja, c/Luis de Ulloa, 20, 26004 Logrono, La Rioja, Spain. ' Dept. Ingenieria Mecanica, Universidad de La Rioja, c/Luis de Ulloa, 20, 26004 Logrono, La Rioja, Spain. ' Dept. Ingenierias Mecanica, Informatica y Aeroespacial, Escuela de Ingenierias Industrial e Informatica, Universidad de Leon, Campus de Vegazana, s.n., 24071 Leon, Spain. ' Dept. Ingenieria de Organizacion, Administracion de Empresas y Estadistica, Escuela Tecnica Superior de Ingenieros Industriales, Universidad Politecnica de Madrid, c/Jose Gutierrez Abascal, 2, 28006 Madrid, Spain
Abstract: In the industrial arena, artificial neural networks are among the most significant techniques in system modelling because of their efficiency and simplicity. In this paper, we present an application of artificial neural networks, along with other techniques stemming from data mining, to model the yield strength, tensile strength, elongation, strain hardening coefficient and the Lankford|s anisotropy coefficient of galvanised steel coils, according to the manufacturing process data. In particular, we propose the use of these models to improve the current control systems of hot-dip galvanising lines since an open loop control strategy must be adopted because the mechanical properties of hot-dip galvanising coils are not directly measurable.
Keywords: hot dip galvanised steel; mechanical properties; artificial neural networks; ANNs; data mining; steel coils.
International Journal of Data Mining, Modelling and Management, 2011 Vol.3 No.4, pp.389 - 405
Published online: 26 Feb 2015 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article