| dc.creator |
García Mateo, Carlos |
|
| dc.creator |
Sourmail, T. |
|
| dc.creator |
García Caballero, Francisca |
|
| dc.creator |
Capdevila, Carlos |
|
| dc.creator |
García de Andrés, Carlos |
|
| dc.date |
2008-03-10T14:58:35Z |
|
| dc.date |
2008-03-10T14:58:35Z |
|
| dc.date |
2005 |
|
| dc.date.accessioned |
2017-01-31T01:00:38Z |
|
| dc.date.available |
2017-01-31T01:00:38Z |
|
| dc.identifier |
Materials Science and Technology 2005 VOL 21 NO 8, 934-940 |
|
| dc.identifier |
http://www.ingentaconnect.com/content/maney/mst |
|
| dc.identifier |
http://hdl.handle.net/10261/3193 |
|
| dc.identifier |
10.1179/174328405X51622 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3193 |
|
| dc.description |
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 |
|
| dc.description |
The authors acknowledge the financial support from the
Spanish Ministerio de Ciencia y Tecnologı´a (project-
MAT 2001-1617). F. G. Caballero would like to thank
the Spanish Ministerio de Ciencia y Tecnologı´a for the
financial support in the form of a Ramo´ n y Cajal
contract (Programa RyC 2002). |
|
| dc.description |
Peer reviewed |
|
| dc.format |
175015 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Institute of Materials, Minerals and Mining |
|
| dc.rights |
openAccess |
|
| dc.subject |
Thermodynamics theory |
|
| dc.subject |
Bainite start temperature |
|
| dc.subject |
Neural network |
|
| dc.subject |
Bayesian framework |
|
| dc.title |
New approach for the bainite start temperature calculation in steels |
|
| dc.type |
Artículo |
|