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This research was partially funded by NAWA-Narodowa Agencja Wymiany Akademickiej, under grant PPN/ULM/2020/1/00125 and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No 734205–H2020-MSCA-RISE-2016. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UID-B/05256/2020, and UID-P/05256/2020, the Center for Mathematical Sciences Applied to Industry (CeMEAI) and the support from the São Paulo Research Foundation, Brazil (FAPESP grant No 2013/07375-0, the Center for Artificial Intelligence (C4AI-USP), the support from the São Paulo Research Foundation, Brazil (FAPESP grant No 2019/07665-4) and the IBM Corporation.
Single screw polymer extrusion can be seen as a multi-objective optimization problem where a set of design variables must be defined as a function of objectives and constraints that are to be satisfied simultaneously. The development of powerful modelling routines based on the use of numerical methods allows linking those objectives with the decision variables. In reality, only a single solution can be used in the problem under consideration. However, the computation times become prohibitive when effective optimization algorithms dealing with multi-objectives and decision-making are to be used, such as those based on populations of solutions. It is proposed here the use of Artificial Intelligence techniques to determine the interrelation between the design variables and the objectives. For that, a data analysis technique, named DAMICORE, was used to define these interrelations. Examples, involving the design of a screw extruder, a barrel grooves section, and a rotational barrel segment, were investigated using the proposed AI techniques. The results obtained show a good correspondence with the expected thermomechanical behaviour of the process. This constitutes an initial step in the application of AI techniques in different fields of engineering in the way of accomplishing, in the future, optimization based on the use of available data.
