1 research outputs found
Applications of Artificial Neural Networks (ANNs) in exploring materials property-property correlations
The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorThe discoveries of materials property-property correlations usually require prior
knowledge or serendipity, the process of which can be time-consuming, costly,
and labour-intensive. On the other hand, artificial neural networks (ANNs) are
intelligent and scalable modelling techniques that have been used extensively to
predict properties from materialsâ composition or processing parameters, but are
seldom used in exploring materials property-property correlations. The work
presented in this thesis has employed ANNs combinatorial searches to explore the
correlations of different materials properties, through which, âknownâ correlations
are verified, and âunknownâ correlations are revealed. An evaluation criterion is
proposed and demonstrated to be useful in identifying nontrivial correlations.
The work has also extended the application of ANNs in the fields of data
corrections, property predictions and identifications of variablesâ contributions. A
systematic ANN protocol has been developed and tested against the known
correlating equations of elastic properties and the experimental data, and is found
to be reliable and effective to correct suspect data in a complicated situation where
no prior knowledge exists. Moreover, the hardness increments of pure metals due
to HPT are accurately predicted from shear modulus, melting temperature and
Burgers vector. The first two variables are identified to have the largest impacts
on hardening. Finally, a combined ANN-SR (symbolic regression) method is
proposed to yield parsimonious correlating equations by ruling out redundant
variables through the partial derivatives method and the connection weight
approach, which are based on the analysis of the ANNs weight vectors. By
applying this method, two simple equations that are at least as accurate as other
models in providing a rapid estimation of the enthalpies of vaporization for
compounds are obtained.School of Engineering and Materials Science of Queen
Mary, University of London and China Scholarship Council (CSC), for providing
Queen Mary - China Scholarship Council Joint PhD Scholarsh