Neurofuzzy and SUPANOVA modelling of structure-property relationships in Al-Zn-Mg-Cu alloys

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Description/Abstract

Neurofuzzy and SUPANOVA data modelling approaches have been used to determine models for yield strength and electrical conductivity from a series of experimental trials. In light of established understanding of the precipitation sequences characterising the 7xxx system, transformations of the compositional levels of important alloying elements have been derived to augment the experimental data, providing better characterisation of the main strengthening and physical characteristics of the alloys. The structure-property models identified by the neurofuzzy and SUPANOVA frameworks have been shown to lead to improvements over simple linear regression analyses, both in terms of the approximation to the experimental observations and in terms of the structure of the relationships identified. The transparency of these empirical techniques has enabled the resulting models to be validated against physical/metallurgical understanding.