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The performance of the single screw polymer extrusion process depends on the definition of the
best set of design variables, including operating conditions and/or geometrical parameters, which
can be seen as a multi-objective optimization problem where several objectives and constraints
must be satisfied simultaneously. The most efficient way to solve this problem consists in linking
a modelling routine with optimization algorithms able to deal with multi-objectives, for example,
those based on a population of solutions. This implies that the modelling routine must be run
several times, and, thus, the computation time can become expensive, since they are based on the
use of sophisticated numerical methods due to the need to obtain reliable results [1]. The aim of
this work is to present an alternative based on the use of Artificial Intelligence (AI) techniques in
order to reduce the number of modelling evaluations required during the optimization process.
This analysis will be based on the use of a data analysis technique, named DAMICORE, able to
define important interrelations between all variables related to extrusion and, then, optimize the
process [2,3,4]. For that purpose, the results obtained for three practical examples will be presented
and discussed. These case studies include the optimization of screw geometrical parameters, barrel
grooves section and rotational barrel segment. It will be shown that the results obtained, taking
into consideration the design variables, the objectives and the constraints defined, are in agreement
with the expected thermomechanical behaviour of the process.
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