Abstract
Rheological structure-property models play a crucial
role in the manufacturing and processing of polymers.
Traditionally rheological models are developed by
design of experiments that measure a rheological
property as a function of the moments of molar mass
distributions. These empirical models lack the capacity
to apply to a wide range of distributions due the
limited availability of experimental data. In recent
years fundamental models were developed to satisfy a
wider range of distributions, but they are in terms of
variables not readily available during processing or
manufacturing. Genetic programming can be used to
bridge the gap between the practical, but limited,
empirical models and the more general, but less
practical, fundamental models. This is a novel approach
of generating rheological models that are both
practical and valid for a wide set of distributions.
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