Radu Calin Dimitriu Modelling the Hot-Strength of Creep-Resistant Ferritic Steels and Relationship to Creep-Rupture.

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Presentation transcript:

Radu Calin Dimitriu Modelling the Hot-Strength of Creep-Resistant Ferritic Steels and Relationship to Creep-Rupture Data

Outline: - 1 st Part The Technique - 2 nd Part The Model

The Technique Neural networks in a Bayesian framework was employed to model the residual stress A neural network is a non-linear method of regression In neural networks data are fitted to a function in order to capture the complex relations that exist between the inputs and the output

Structure of a three layer neural network

Input variables C, Mn, Si, Ni, Cr, Mo, V, Co, B, N, O, … Thermomechanical processing of steel Subsequent heat treatment Steel properties Operating conditions...

The general form of the neural network y – represents the output w – represents the weights h – represents the hidden units θ – represents the bias x – represents the inputs i, j – represent subscripts

Hyperbolic Tangents

Problem Powerful non-linear regression methods → overfitting

Training and testing data sets

Advantage of neural networks in a Bayesian framework - the uncertainties are quantified - ant then transformed in error bars

Outline: - 1 st Part The Technique - 2 nd Part The Model

Significance

Predictions

Calculations

Correlation

ACKNOWLEDGEMENTS Many thanks to professor H.K.D.H. Bhadeshia The Department of Material Science and Metallurgy The University of Cambridge The European Commission under the Marie Curie Early Stage Research Training Programme