Prediction of martensite fraction of microalloyed steel by artificial neural networks

Title:

Prediction of martensite fraction of microalloyed steel by artificial neural networks

Creator:

Khalaj, Gholamreza, Nazari, Ali, and Pouraliakbar, Hesam

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:d186fee5-09e1-454b-a5f2-02583744cb8c
uuid:d186fee5-09e1-454b-a5f2-02583744cb8c
doi:10.14311/NNW.2013.23.009

Subject:

Artificial neural network (ANN), microalloyed steel, continuous cooling, and low-carbon Nb steel

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The final microstructure and resulting mechanical properties in the linepipe steels are predominantly determined by austenite decomposition during cooling after thermomechanical and welding processes. The paper presents some results of the research connected with the development of a new approach based on the artificial neural network to predicting the martensite fraction of the phase constituents occurring in five microalloyed steels after continuous cooling. The independent variables in the model are chemical compositions, niobium condition, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For the purpose of constructing these models, 104 different experimental data were gathered from the literature. According to the input parameters in feedforward backpropagation algorithm, the constructed networks were trained, validated and tested. In this model, the training and testing results in the artificial neural network have shown a strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.

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:2

Harvested from:

CDK

Metadata only:

false