| dc.creator |
Sourmail, T. |
|
| dc.creator |
García Mateo, Carlos |
|
| dc.date |
2008-03-10T14:51:31Z |
|
| dc.date |
2008-03-10T14:51:31Z |
|
| dc.date |
2005 |
|
| dc.date.accessioned |
2017-01-31T01:00:38Z |
|
| dc.date.available |
2017-01-31T01:00:38Z |
|
| dc.identifier |
Computational Materials Science 34 (2005) 323-334 |
|
| dc.identifier |
http://hdl.handle.net/10261/3191 |
|
| dc.identifier |
10.1016/j.commatsci.2005.01.002 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3191 |
|
| dc.description |
Different approaches to predicting the Ms temperatures of steels are reviewed and discussed with the objective of
summarising the main characteristics, advantages and difficulties of each method, mostly from a practical point of view.
Empirical methods, and methods based on thermodynamics are then assessed against published data. |
|
| dc.description |
NPL for provision
of MTDATA and Neuromat for provision of the
Model Manager |
|
| dc.description |
Peer reviewed |
|
| dc.format |
246969 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Elsevier |
|
| dc.relation |
http://dx.doi.org/10.1016/j.commatsci.2005.01.002 |
|
| dc.rights |
openAccess |
|
| dc.subject |
Martensite; Thermodynamics; Bayesian neural networks; Linear regression |
|
| dc.title |
Critical assessment of models for predicting the Ms temperature of steels |
|
| dc.type |
Artículo |
|