Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels
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 perc...
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| Format: | Article |
| Language: | English |
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University of Belgrade, Technical Faculty, Bor
2015-01-01
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| Series: | Journal of Mining and Metallurgy. Section B: Metallurgy |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/1450-5339/2015/1450-53391500025P.pdf |
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| author | Pouraliakbar H. Khalaj G. Jandaghi M.R. Khalaj M.J. |
| author_facet | Pouraliakbar H. Khalaj G. Jandaghi M.R. Khalaj M.J. |
| author_sort | Pouraliakbar H. |
| collection | DOAJ |
| description | 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. The
developed model was validated with 26 other steels from the data set that
were not used for the model development. Additionally, the model was also
employed to predict the toughness of 26 newly tested steels. The predicted
values were in very good agreement with the measured ones indicating that
the developed model was very accurate and had the great ability for
predicting the toughness of pipeline steels. |
| format | Article |
| id | doaj-art-c01d7fb2fee54b428c66a4ba70d944a5 |
| institution | Kabale University |
| issn | 1450-5339 2217-7175 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | University of Belgrade, Technical Faculty, Bor |
| record_format | Article |
| series | Journal of Mining and Metallurgy. Section B: Metallurgy |
| spelling | doaj-art-c01d7fb2fee54b428c66a4ba70d944a52025-02-02T04:11:06ZengUniversity of Belgrade, Technical Faculty, BorJournal of Mining and Metallurgy. Section B: Metallurgy1450-53392217-71752015-01-0151217317810.2298/JMMB140525025P1450-53391500025PStudy on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steelsPouraliakbar H.0Khalaj G.1Jandaghi M.R.2Khalaj M.J.3World Tech Scientific Research Center (WT-SRC), Department of Advanced Materials, Tehran, IranSaveh Branch, Islamic Azad University, Department of Materials Engineering, Saveh, IranWorld Tech Scientific Research Center (WT-SRC), Department of Advanced Materials, Tehran, IranSaveh Branch, Islamic Azad University, Department of Materials Engineering, Saveh, IranIn 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. The developed model was validated with 26 other steels from the data set that were not used for the model development. Additionally, the model was also employed to predict the toughness of 26 newly tested steels. The predicted values were in very good agreement with the measured ones indicating that the developed model was very accurate and had the great ability for predicting the toughness of pipeline steels.http://www.doiserbia.nb.rs/img/doi/1450-5339/2015/1450-53391500025P.pdfartificial neural networkmodelingtoughnesstensile testmicroalloyed steelchemical composition |
| spellingShingle | Pouraliakbar H. Khalaj G. Jandaghi M.R. Khalaj M.J. Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels Journal of Mining and Metallurgy. Section B: Metallurgy artificial neural network modeling toughness tensile test microalloyed steel chemical composition |
| title | Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels |
| title_full | Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels |
| title_fullStr | Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels |
| title_full_unstemmed | Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels |
| title_short | Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels |
| title_sort | study on the correlation of toughness with chemical composition and tensile test results in microalloyed api pipeline steels |
| topic | artificial neural network modeling toughness tensile test microalloyed steel chemical composition |
| url | http://www.doiserbia.nb.rs/img/doi/1450-5339/2015/1450-53391500025P.pdf |
| work_keys_str_mv | AT pouraliakbarh studyonthecorrelationoftoughnesswithchemicalcompositionandtensiletestresultsinmicroalloyedapipipelinesteels AT khalajg studyonthecorrelationoftoughnesswithchemicalcompositionandtensiletestresultsinmicroalloyedapipipelinesteels AT jandaghimr studyonthecorrelationoftoughnesswithchemicalcompositionandtensiletestresultsinmicroalloyedapipipelinesteels AT khalajmj studyonthecorrelationoftoughnesswithchemicalcompositionandtensiletestresultsinmicroalloyedapipipelinesteels |