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

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

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

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

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

A model for the electrical conductivity of peak-aged and overaged Al-Zn-Mg-Cu alloys

A physically based model for the electrical conductivity of peak-aged and overaged Al-Zn-Mg-Cu (7xxx series) alloys is presented. The model includes calculations of the ?- and the S-phase solvus (using a regular-solution model), taking account of the capillary effect and ? coarsening. It takes account of the conductivity of grains (incorporating dissolved alloying elements, undissolved particles, and precipitates) and solute-depleted areas at the grain boundaries. Data from optical microscopy, differential scanning calorimetry (DSC), scanning electron microscopy (SEM) with energy-dispersive X-ray spectrometry (EDS), and transmission electron microscopy (TEM) are consistent with the model and its predictions. The model has been successfully used to fit and predict the conductivity data of a set of 7xxx alloys including both Zr-containing alloys and Cr-containing alloys under various aging conditions, achieving an accuracy of about 1 pct in predicting unseen conductivity data from this set of alloys

Effects of carbides on fatigue characteristics of austempered ductile iron

Crack initiation and growth behavior of an austempered ductile iron (ADI) austenitized at 800 °C and austempered at 260 °C have been assessed under three-point bend fatigue conditions. Initiation sites have been identified as carbides remaining from the as-cast ductile iron due to insufficient austenization. The number of carbides cracking on loading to stresses greater than 275 MPa is critical in determining the failure mechanism. In general, high carbide area fractions promote coalescence-dominated fatigue crack failure, while low area fractions promote propagation-dominated fatigue crack failure. Individual carbides have been characterized using finite body tessellation (FBT) and adaptive numerical modeling (SUpport vector Parsimonious ANalysis Of VAriance (SUPANOVA)) techniques in an attempt to quantify the factors promoting carbide fracture. This indicated that large or long and thin carbides on the whole appear to be susceptible to fracture, and carbides that are locally clustered and aligned perpendicular to the tensile axis are particularly susceptible to fracture