2015
DOI: 10.2298/jmmb140525025p
|View full text |Cite
|
Sign up to set email alerts
|

Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels

Abstract: In this investigation, an artificial neural network model with feed forward topology and back propagation algorithm was developed to predict the toughness (area underneath of stress-strain curve) of high strength low alloy steels. The inputs of the neural network included the weight percentage of 15 alloying elements and the tensile test results such as yield strength, ultimate tensile strength and elongation. Developing the model, 118 different steels from API X52 to X70 grades were used. Th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
14
0

Year Published

2017
2017
2022
2022

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 37 publications
(14 citation statements)
references
References 10 publications
(10 reference statements)
0
14
0
Order By: Relevance
“…The atoms of Cr, Mo and Ni in the weld metal can be remelted at a high temperature, which can replace Fe atoms in the lattice and disturb the original lattice arrangement, and can also make dislocation movement difficult and strengthen the joint. As in HSLA steel, the addition of Mn and other alloying elements, such as copper (Cu), titanium (TI) and vanadium (V), both provide strengthening and an obtain ideal microstructure [26,27];…”
Section: Charpy V-notch Impact Testsmentioning
confidence: 99%
“…The atoms of Cr, Mo and Ni in the weld metal can be remelted at a high temperature, which can replace Fe atoms in the lattice and disturb the original lattice arrangement, and can also make dislocation movement difficult and strengthen the joint. As in HSLA steel, the addition of Mn and other alloying elements, such as copper (Cu), titanium (TI) and vanadium (V), both provide strengthening and an obtain ideal microstructure [26,27];…”
Section: Charpy V-notch Impact Testsmentioning
confidence: 99%
“…In addition to these two basic approaches, the use of so-called artificial neutral networks (ANN) in the material science has become more widespread in recent years, e.g., Pouraliakbar [35] developed an ANN model to predict the toughness of HSLA steel. Nowadays, in light of springback prediction, utilisation of ANN represents an alternative tool for springback prediction, especially regarding that in light of nonlinear recovery, FEM has become quite complicated to achieve reliable results as it is concluded, e.g., by [36].…”
Section: Introductionmentioning
confidence: 99%
“…Further, Khalaj and Pouraliakbar et al investigated the correlation of passivation current density and potential using chemical composition and corrosion cell characteristics in HSLA steels by means of an artificial neural network approach, and the influence of physical and chemical parameters on toughness of pipeline steel is simulated by high-precision model. This provides a basis for the application and corrosion monitoring of pipeline steel [ 27 , 28 ]. A significant amount of research has also been reported by domestic and international scholars on the effects of capillary action [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”
Section: Introductionmentioning
confidence: 99%