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Título: | New approach for the bainite start temperature calculation in steels |
Autor: | García Mateo, Carlos CSIC ORCID CVN ; Sourmail, Thomas; García Caballero, Francisca CSIC ORCID ; Capdevila, Carlos CSIC ORCID ; García de Andrés, Carlos | Palabras clave: | Thermodynamics theory Bainite start temperature Neural networks Bayesian framework |
Fecha de publicación: | 2005 | Editor: | Institute of Materials, Minerals and Mining | Citación: | Materials Science and Technology 2005 VOL 21 NO 8, 934-940 http://www.ingentaconnect.com/content/maney/mst |
Resumen: | The bainite start temperature Bs is defined as the highest temperature at which ferrite can transform by a displacive transformation. A common observation is that the bainite start temperature is very sensitive to the chemical composition, indicating that the influence of solutes is more than just thermodynamic. Empirical linear regression models have long been used to calculate the Bs in a limited range of compositions. This paper attempts to create an empirical model of wider applicability and higher accuracy by means of neural networks. The results are compared with those calculated using the thermodynamic theory for bainite transformation, revealing that in general this theory agrees with the experimental results, but some discrepancies can still be found when the alloys are heavily alloyed | URI: | http://hdl.handle.net/10261/3193 | DOI: | 10.1179/174328405X51622 |
Aparece en las colecciones: | (CENIM) Artículos |
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Mat_Sci_tech_2005_21_934.pdf | 170,91 kB | Adobe PDF | Visualizar/Abrir |
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