Kandola, J.S., Gunn, S.R., Sinclair, I. and Reed, P.A.S.
Data Driven Knowledge Extraction of Materials Properties.
At Intelligent Processing and Manufacturing of Materials, Hawaii, U.S.A.,
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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