Development of neural network-based models to predict mechanical properties of hot dip galvanised steel coils

Ana Gonzalez-Marcos, Fernando Alba-Elias, Manuel Castejon-Limas and Joaquin Ordieres-Mere

International Journal of Data Mining, Modelling and Management, 2011, vol. 3, issue 4, 389-405

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. (search for similar items in EconPapers)
Date: 2011
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