Neural network model for improvement of strength–ductility compromise in low carbon sheet steels
Authors: Capdevila, C.; Garcia-Mateo, C.; Caballero, F. G.; de Andrés, C. García
Source: Materials Science and Technology, Volume 22, Number 10, October 2006 , pp. 1163-1170(8)
Publisher: Maney Publishing
Abstract:The goal of the work reported in the present paper is to develop a neural network model for describing the evolution of the compromise (UTS × EL) between ultimate tensile strength (UTS), and elongation (EL) mechanical properties on low carbon sheet steels. The model presented here take into account the influence of 21 parameters describing chemical composition, and thermomechanical processes such as austenite and ferrite rolling, coiling, cold working and subsequent annealing involved in the production route of low carbon steels. The results presented in the present paper demonstrate that this model can help with optimising simultaneously both strength and ductility for the various types of forming operation that the sheets can be subjected to.
Document Type: Research Article
Publication date: 2006-10-01
Authors wishing to cite fast track papers should give the journal name and the article DOI. This will enable reference linking via CrossRef and allow forward and backward citation tracking systems to associate the fast track citation with the final journal reference.Materials Science and Technology is the successor of two previous titles, for which digitised archives are available: Metal Science (Vols. 1—17; 1967—84) and Metals Technology (Vols. 1—11; 1974—84).
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