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

Abstract

The paper deals with the application of multiple linear regression and neurofuzzy modelling approaches to 7xxx series based aluminum 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 initializing 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 initialized neurofuzzy models and input data transformations to provide both optimal subset and feature representation.