Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3191
Title: Critical assessment of models for predicting the Ms temperature of steels
Keywords: Martensite; Thermodynamics; Bayesian neural networks; Linear regression
Publisher: Elsevier
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.
NPL for provision of MTDATA and Neuromat for provision of the Model Manager
Peer reviewed
URI: http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3191
Other Identifiers: Computational Materials Science 34 (2005) 323-334
http://hdl.handle.net/10261/3191
10.1016/j.commatsci.2005.01.002
Appears in Collections:Digital Csic

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