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Adaptive numerical modelling of commercial aluminium plate performance

Adaptive numerical modelling of commercial aluminium plate performance
Adaptive numerical modelling of commercial aluminium plate performance
Adaptive numerical methods, such as neural networks, have received considerable attention in recent years in relation to the modelling of complex physical systems. In this work a variety of such methods have been applied to the modelling/data mining of commercial materials production data, thereby avoiding the scale-up problems associated with laboratory scale investigations of materials behaviour. It is shown that adaptive numerical methods may determine valuable empirical models from such complex databases, whilst the value of transparent modelling methods (where the underlying relationships between input variables and modelled characteristics may be clearly visualised) is highlighted in providing model confidence and the potential to extract novel physical understanding.
533-538
Christensen, S.W.
c5995cdb-2a14-478a-8c98-6c9f11d65b09
Kandola, J.S.
c976459a-d502-4688-b741-334c06796ca8
Femminella, O.P.
be8a8548-2f97-4d11-bd06-012e27609627
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Sinclair, I.
b7a58838-8f58-4010-a06b-91c6512bcfaa
Starke, E.A.
Sanders, T.H.
Cassada, W.A.
Christensen, S.W.
c5995cdb-2a14-478a-8c98-6c9f11d65b09
Kandola, J.S.
c976459a-d502-4688-b741-334c06796ca8
Femminella, O.P.
be8a8548-2f97-4d11-bd06-012e27609627
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Sinclair, I.
b7a58838-8f58-4010-a06b-91c6512bcfaa
Starke, E.A.
Sanders, T.H.
Cassada, W.A.

Christensen, S.W., Kandola, J.S., Femminella, O.P., Gunn, S.R., Reed, P.A.S. and Sinclair, I. (2000) Adaptive numerical modelling of commercial aluminium plate performance. In, Starke, E.A., Sanders, T.H. and Cassada, W.A. (eds.) Materials Science Forum. Aluminium Alloys: Their Physical and Mechanical Properties (01/11/00) pp. 533-538.

Record type: Book Section

Abstract

Adaptive numerical methods, such as neural networks, have received considerable attention in recent years in relation to the modelling of complex physical systems. In this work a variety of such methods have been applied to the modelling/data mining of commercial materials production data, thereby avoiding the scale-up problems associated with laboratory scale investigations of materials behaviour. It is shown that adaptive numerical methods may determine valuable empirical models from such complex databases, whilst the value of transparent modelling methods (where the underlying relationships between input variables and modelled characteristics may be clearly visualised) is highlighted in providing model confidence and the potential to extract novel physical understanding.

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More information

Published date: November 2000
Venue - Dates: Aluminium Alloys: Their Physical and Mechanical Properties, 2000-11-01
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 252079
URI: https://eprints.soton.ac.uk/id/eprint/252079
PURE UUID: 61ed082e-9794-45b5-9bf8-369c1018a3df
ORCID for P.A.S. Reed: ORCID iD orcid.org/0000-0002-2258-0347

Catalogue record

Date deposited: 29 Nov 2003
Last modified: 29 Nov 2018 01:35

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