Neural network analysis of the influence of processing on strength and ductility of automotive low carbon sheet steels
- 30 March 2006
- journal article
- Published by Elsevier BV in Computational Materials Science
- Vol. 38 (1), 192-201
- https://doi.org/10.1016/j.commatsci.2006.02.005
Abstract
The goal of the work reported in this paper is to develop a neural network model for describing the evolution of mechanical properties such as yield strength (YS), ultimate tensile strength (UTS), and elongation (EL) on low carbon sheet steels. The models 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 on the production route of low carbon steels. The results presented in this paper demonstrate that these models can help on optimizing simultaneously both strength and ductility for the various types of forming operation that the sheets can be subjected to.Keywords
This publication has 7 references indexed in Scilit:
- Influence of Second Phase Particles on Recrystallisation of Cold-Rolled Low Carbon Microalloyed Steels during Isothermal AnnealingMaterials Science Forum, 2005
- Evaluation of the Austenitic Grain Growth by Thermoelectric Power MeasurementsMaterials Science Forum, 2004
- Austenite Grain Coarsening Under the Influence of Niobium CarbonitridesMATERIALS TRANSACTIONS, 2004
- Neural Networks in Materials Science.ISIJ International, 1999
- A Practical Bayesian Framework for Backpropagation NetworksNeural Computation, 1992
- BAYESIAN INTERPOLATIONNeural Computation, 1992
- Development and control of annealing textures in low-carbon steelsInternational Metals Reviews, 1984