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Data driven knowledge extraction of materials properties

Data driven knowledge extraction of materials properties
Data driven knowledge extraction of materials properties
In this paper the problem of modelling a large commercial materials dataset using advanced adaptive numeric methods is described. The various approaches are briefly outlined, with an emphasis on their characteristics with respect to generalisation, performance and transparency. A highly novel Support Vector Machine (SVM) approach incorporating a high degree of transparency via a full ANalysis Of VAriance (ANOVA) expansion is also used. Using the example of predicting 0.2% proof stress from a set of materials features, we show how the different modelling techniques compare when benchmarked against independent test data.
361-366
Kandola, J.S.
c976459a-d502-4688-b741-334c06796ca8
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Sinclair, I.
b7a58838-8f58-4010-a06b-91c6512bcfaa
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Kandola, J.S.
c976459a-d502-4688-b741-334c06796ca8
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Sinclair, I.
b7a58838-8f58-4010-a06b-91c6512bcfaa
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17

Kandola, J.S., Gunn, S.R., Sinclair, I. and Reed, P.A.S. (1999) Data driven knowledge extraction of materials properties. Intelligent Processing and Manufacturing of Materials, Hawaii, United States. pp. 361-366 .

Record type: Conference or Workshop Item (Other)

Abstract

In this paper the problem of modelling a large commercial materials dataset using advanced adaptive numeric methods is described. The various approaches are briefly outlined, with an emphasis on their characteristics with respect to generalisation, performance and transparency. A highly novel Support Vector Machine (SVM) approach incorporating a high degree of transparency via a full ANalysis Of VAriance (ANOVA) expansion is also used. Using the example of predicting 0.2% proof stress from a set of materials features, we show how the different modelling techniques compare when benchmarked against independent test data.

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More information

Published date: July 1999
Additional Information: Accepted for Publication at IPMM 99 CD-ROM. Organisation: IEEE
Venue - Dates: Intelligent Processing and Manufacturing of Materials, Hawaii, United States, 1999-07-01
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 252054
URI: https://eprints.soton.ac.uk/id/eprint/252054
PURE UUID: 90d90a25-d044-4e2c-b731-03da09dc10c7
ORCID for P.A.S. Reed: ORCID iD orcid.org/0000-0002-2258-0347

Catalogue record

Date deposited: 29 Nov 2003
Last modified: 06 Oct 2018 00:39

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