Modeling the correlation between yield strength, chemical composition and ultimate tensile strength of X70 pipeline steels by means of gene expression programming

被引:12
作者
Khalaj, Gholamreza [1 ]
Khalaj, Mohammad-Javad [1 ]
机构
[1] Islamic Azad Univ, Dept Tech & Engn Sci, Saveh Branch, Saveh, Iran
关键词
Microalloyed steel; Mechanical properties; API X70 steel; Gene expression programming; FUNCTIONALLY GRADED STEELS; NEURAL-NETWORKS; TRANSITION-TEMPERATURE; LOW-CARBON; TRANSFORMATION;
D O I
10.3139/146.110910
中图分类号
TF [冶金工业];
学科分类号
0806 ;
摘要
In the present work, the ultimate tensile strength of steel made using thermomechanically controlled processing has been modeled by means of gene expression programming. To build the model, training and testing using experimental results from 104 specimens were conducted. The data used as inputs in gene expression programming models are arranged in a format of six parameters that cover the carbon equivalent, based upon the International Institute of Welding equation and the chemical portion of the Ito-Bessyo equation, the sum of the Nb, V, and Ti, the sum of the Nb and V, the sum of the Cr, Mo, Ni, and Cu contents and yield strength. The training and testing results in gene expression programming models have shown a strong potential for correlating the ultimate tensile strength to yield strength and chemical composition of X70 pipeline steels.
引用
收藏
页码:697 / 702
页数:6
相关论文
共 22 条
[1]  
*AM PETR I, 2007, API SPEC 5L SPEC LIN
[2]   COGNITIVE AND PSYCHOLOGICAL COMPUTATION WITH NEURAL MODELS [J].
ANDERSON, JA .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1983, 13 (05) :799-815
[3]   The effect of chemical composition and austenite conditioning on the transformation behavior of microalloyed steels [J].
Anijdan, S. H. Mousavi ;
Rezaeian, Ahmad ;
Yue, Steve .
MATERIALS CHARACTERIZATION, 2012, 63 :27-38
[4]  
Anijdan S.H. Mousavi., 2009, MAT SCI TECHNOLOGY M, P1308
[5]  
[Anonymous], 1997, ANN BOOK ASTM STAND, V3.01, p[A751, E8, E23, E45]
[6]  
[Anonymous], 2003, Genetic programming IV: routine human-competitive machine intelligence
[7]   Influence of the chemical composition on transformation behaviour of low carbon microalloyed steels [J].
Calvo, J. ;
Jung, I. -H. ;
Elwazri, A. M. ;
Bai, D. ;
Yue, S. .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2009, 520 (1-2) :90-96
[8]   Neural network analysis of Charpy transition temperature of irradiated low-activation martensitic steels [J].
Cottrell, G. A. ;
Kemp, R. ;
Bhadeshia, H. K. D. H. ;
Odette, G. R. ;
Yamamoto, T. .
JOURNAL OF NUCLEAR MATERIALS, 2007, 367 :603-609
[9]  
Gladman T., 1997, PHYS METALLURGY MICR, V1st
[10]   NEURAL NETWORKS AND PHYSICAL SYSTEMS WITH EMERGENT COLLECTIVE COMPUTATIONAL ABILITIES [J].
HOPFIELD, JJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1982, 79 (08) :2554-2558