Modeling the correlation between heat treatment, chemical composition and bainite fraction of pipeline steels by means of artifcial neural networks

Title:

Modeling the correlation between heat treatment, chemical composition and bainite fraction of pipeline steels by means of artifcial neural networks

Creator:

Khalaj, Gholamreza, Pauraliakbar, Hesam, Mamaghani, Kaveh Rahimi, and Khalaj, Mohammad-Javad

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:a107ce63-cb35-4507-ae71-2fbbed123a75
uuid:a107ce63-cb35-4507-ae71-2fbbed123a75
doi:10.14311/NNW.2013.23.022

Subject:

ANN, HSLA, pipeline steel, microalloyed steel, and bainite transformation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In the present study, bainite fraction results of continuous cooling of high strength low alloy steels have been modeled by artificial neural networks. The artificial neural network models were constructed by 16 input parameters including chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), Nb in solution, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. The value for the output layer was the bainite fraction. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. To make a decision on the completion of the training processes, two termination states are declared: state 1 (ANN-I model) means that the training of neural network was ended when the maximum epoch of process reached (1000) while state 2 (ANN-II model) means the training ended when minimum error norm of network gained. The entire statistical evaluators of ANN-II model has higher performance than those of ANN-I. However, both of the models exhibit valuable results and the entire statistical values show that the proposed ANN-I and ANN-II models are suitably trained and can predict the bainite fraction values very close to the experimental ones.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2013 Volume:23 | Number:4

Harvested from:

CDK

Metadata only:

false