Neural network analysis of the influence of processing on strength and ductility of automotive low carbon sheet steels

被引：34
作者：
Capdevila, C. [1 ]
Garcia-Mateo, C. [1 ]
Caballero, F. G. [1 ]
Garcia de Andres, C. [1 ]
机构：
[1] CSIC, CENIM, Dept Met Phys, MATERALIA Res Grp, Madrid 28040, Spain
来源：
COMPUTATIONAL MATERIALS SCIENCE | 2006年 / 38卷 / 01期
关键词：
neural network; strength; ductility; low carbon steels; processing parameters;
D O I：
10.1016/j.commatsci.2006.02.005
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：192 / 201
页数：10
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