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An example of the use of neural computing techniques in materials science: the modelling of fatigue thresholds in Ni-base superalloys

An example of the use of neural computing techniques in materials science: the modelling of fatigue thresholds in Ni-base superalloys
An example of the use of neural computing techniques in materials science: the modelling of fatigue thresholds in Ni-base superalloys
Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network’s complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules.
neural computing, superalloy, fuzzy rules, fatigue threshold
0921-5093
222-239
Schooling, J.M.
1bc4491f-376d-4fb5-b6b6-204bb2448199
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Schooling, J.M.
1bc4491f-376d-4fb5-b6b6-204bb2448199
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17

Schooling, J.M., Brown, M. and Reed, P.A.S. (1999) An example of the use of neural computing techniques in materials science: the modelling of fatigue thresholds in Ni-base superalloys. Materials Science and Engineering: A, 260 (1-2), 222-239. (doi:10.1016/S0921-5093(98)00957-5).

Record type: Article

Abstract

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network’s complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules.

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More information

Published date: February 1999
Keywords: neural computing, superalloy, fuzzy rules, fatigue threshold
Organisations: Engineering Sciences

Identifiers

Local EPrints ID: 21644
URI: http://eprints.soton.ac.uk/id/eprint/21644
ISSN: 0921-5093
PURE UUID: d974e885-40ad-45b6-8563-9a9131ba38a4
ORCID for P.A.S. Reed: ORCID iD orcid.org/0000-0002-2258-0347

Catalogue record

Date deposited: 13 Dec 2006
Last modified: 16 Mar 2024 02:44

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Contributors

Author: J.M. Schooling
Author: M. Brown
Author: P.A.S. Reed ORCID iD

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