The application of neural computing methods to the modelling of fatigue in Ni-base superalloys


Schooling, J.M. and Reed, P.A.S. (1996) The application of neural computing methods to the modelling of fatigue in Ni-base superalloys. In, Kissinger, R.D., Deye, D.J., Anton, D.L., Cetel, A.D., Nathal, M.V., Pollock, T.M. and Woodford, D.A. (eds.) Superalloys 1996. Eighth International Symposium on Superalloys Warrendale, USA, TMS, 409-417.

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Description/Abstract

The current financial climate is driving a move towards increased
use of computer modelling techniques in alloy design and
development in order to reduce cost. In this paper the potential for
use of neural computing methods in the prediction of fatigue
resistance in Ni-base superalloys is assessed. Initial work has been
conducted on the Stage II (Paris regime) behaviour, as the literature
indicates that this is the simplest region of the fatigue crack growth
curve to predict, with an approximately linear relationship existing
between log(da/dN and log(AK), and the crack growth rates being
principally affected by temperature, Young’s modulus and yield
strength. These three parameters were chosen for initial data
collection and modelling. The predictions made are of fatigue life,
calculated from the slope and intercept values of the linear portion of
the log-log fatigue crack growth curve. A test dataset has been
successfully predicted along with the trends in the data. The effect
of adding ultimate tensile strength and electron valencies as inputs to
the model is assessed. It is shown that validation of models
produced against metallurgical experience, and careful construction
of the database are important conditions for effective use of neural
network models for fatigue life predictions

Item Type: Book Section
ISBNs: 087339352 (hardback)
Related URLs:
Subjects: T Technology > TN Mining engineering. Metallurgy
Q Science > QA Mathematics > QA76 Computer software
Divisions: University Structure - Pre August 2011 > School of Engineering Sciences
ePrint ID: 21649
Date Deposited: 14 Mar 2007
Last Modified: 27 Mar 2014 18:11
Publisher: TMS
URI: http://eprints.soton.ac.uk/id/eprint/21649

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