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Data pre-processing/model initialisation in neurofuzzy modelling of structure-property relationships in Al-Zn-Mg-Cu alloys

Data pre-processing/model initialisation in neurofuzzy modelling of structure-property relationships in Al-Zn-Mg-Cu alloys
Data pre-processing/model initialisation in neurofuzzy modelling of structure-property relationships in Al-Zn-Mg-Cu alloys
The paper deals with the application of multiple linear regression and neurofuzzy modelling approaches to 7xxx series based aluminium alloys. 36 compositional and ageing time variants and subsequent proof strength and electrical conductivity measurements have been studied. The input datasets have been transformed in two ways: to reveal more explicit microstructural information and to reflect some empirical findings in the literature. Neurofuzzy modelling exhibited improved performance in modelling proof strength and electrical conductivity cf. the multiple linear regression approach. Electrical conductivity is best modelled using the explicit microstructural input dataset, whilst proof strength is best modelled by a further modification of this dataset, decided upon after inspection of the subnetwork structures produced by neurofuzzy modelling. Neurofuzzy modelling offers a transparent empirically based data-driven approach that can be combined with pre-processing of the data and initialising of the model structure based upon physical understanding. An iterative modelling approach is defined whereby data-driven empirical modelling approaches are first used to assess underlying data structures and are validated against physically based understanding, these then inform subsequent initialised neurofuzzy models and input data transformations to provide both optimal subset and feature representation.
neural networks, fuzzy logic, microstructure, 7xxx alloys, electrical conductivity
1027-1037
Femminella, O.P.
be8a8548-2f97-4d11-bd06-012e27609627
Starink, M.J.
14ee8acd-2e6d-4308-a97c-b7c04b27fe40
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Sinclair, I.
b7a58838-8f58-4010-a06b-91c6512bcfaa
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Femminella, O.P.
be8a8548-2f97-4d11-bd06-012e27609627
Starink, M.J.
14ee8acd-2e6d-4308-a97c-b7c04b27fe40
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Sinclair, I.
b7a58838-8f58-4010-a06b-91c6512bcfaa
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17

Femminella, O.P., Starink, M.J., Brown, M., Sinclair, I., Harris, C.J. and Reed, P.A.S. (1999) Data pre-processing/model initialisation in neurofuzzy modelling of structure-property relationships in Al-Zn-Mg-Cu alloys. ISIJ International, 39 (10), 1027-1037. (doi:10.2355/isijinternational.39.1027).

Record type: Article

Abstract

The paper deals with the application of multiple linear regression and neurofuzzy modelling approaches to 7xxx series based aluminium alloys. 36 compositional and ageing time variants and subsequent proof strength and electrical conductivity measurements have been studied. The input datasets have been transformed in two ways: to reveal more explicit microstructural information and to reflect some empirical findings in the literature. Neurofuzzy modelling exhibited improved performance in modelling proof strength and electrical conductivity cf. the multiple linear regression approach. Electrical conductivity is best modelled using the explicit microstructural input dataset, whilst proof strength is best modelled by a further modification of this dataset, decided upon after inspection of the subnetwork structures produced by neurofuzzy modelling. Neurofuzzy modelling offers a transparent empirically based data-driven approach that can be combined with pre-processing of the data and initialising of the model structure based upon physical understanding. An iterative modelling approach is defined whereby data-driven empirical modelling approaches are first used to assess underlying data structures and are validated against physically based understanding, these then inform subsequent initialised neurofuzzy models and input data transformations to provide both optimal subset and feature representation.

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More information

Published date: October 1999
Keywords: neural networks, fuzzy logic, microstructure, 7xxx alloys, electrical conductivity
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 252024
URI: http://eprints.soton.ac.uk/id/eprint/252024
PURE UUID: eb713ac9-17f5-49e4-9ea6-abdfaa12b4a9
ORCID for P.A.S. Reed: ORCID iD orcid.org/0000-0002-2258-0347

Catalogue record

Date deposited: 01 Dec 1999
Last modified: 20 Jul 2019 01:21

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Contributors

Author: O.P. Femminella
Author: M.J. Starink
Author: M. Brown
Author: I. Sinclair
Author: C.J. Harris
Author: P.A.S. Reed ORCID iD

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