Neural Networks in Materials Science.

The bainite start temperature B s 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 B s 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.

show abstract

“…Refs. [19][20][21][22], but presents the main features of the method. The typical structure of a neural network is presented in Fig.…”

Section: Neural Network Modellingmentioning

confidence: 99%

New approach for the bainite start temperature calculation in steels

García‐Mateo¹,

Sourmail²,

Caballero³

et al. 2005

Materials Science and Technology

View full text Add to dashboard Cite

show abstract

“…For this purpose, small concentrations (typically 5-20 ppm) are used in fully de-oxidised steels. 22) However, subsequent tempering and PWHT are likely to result in formation of grain boundary borides (Fe 2 B or M 23 (BC) 6 ) that could adversely affect toughness.…”

Section: Comparison Of Model Predictionsmentioning

confidence: 99%

“…The performance of the test data can be used to optimize the number of units in the hidden layer and effectively match the network complexity to the size and quality of the training and test data sets. 6) Instability of the weights is possible under certain conditions and strategies such as use of a momentum term 5) and smoothing methods 11) have been developed to reduce the risk of instability.…”

mentioning

confidence: 99%

Artificial Neural Networks for Modelling of the Impact Toughness of Steel

Dunne

Tsuei²,

Sterjovski

2004

ISIJ International

View full text Add to dashboard Cite

“…[1][2][3][4][5][6] Indeed, as pointed out by Abrahart et al, 7 aside from materials science, there are reviews or extended summaries of the applications of networks in the fields of hydrological sciences, 8 atmospheric sciences, 9 civil engineering, 10 process engineering 11 and structural engineering. 12 This reflects the generic nature of the method.…”

Section: Introductionmentioning

confidence: 99%

Performance of neural networks in materials science

Bhadeshia¹,

Dimitriu²,

Forsik³

et al. 2009

Materials Science and Technology

Self Cite

115

View full text Add to dashboard Cite

Neural networks are now a prominent feature of materials science with rapid progress in all sectors of the subject. It is premature, however, to claim that the method is established. There are genuine difficulties caused by the often incomplete exploration and publication of models. The assessment presented here is an attempt to compile a loose set of guidelines for maximising the impact of any models that are created, in order to encourage thoroughness in publication to a point where the work can be independently verified.

show abstract

Neural Networks in Materials Science.

Cited by 587 publications

References 9 publications

New approach for the bainite start temperature calculation in steels

New approach for the bainite start temperature calculation in steels

Artificial Neural Networks for Modelling of the Impact Toughness of Steel

Performance of neural networks in materials science

Contact Info

Product

Resources

About