  We present a novel application of neural networks to design improved mixing
elements for single-screw extruders. Specifically, we propose to use neural
networks in numerical shape optimization to parameterize geometries. Geometry
parameterization is crucial in enabling efficient shape optimization as it
allows for optimizing complex shapes using only a few design variables. Recent
approaches often utilize CAD data in conjunction with spline-based methods
where the spline's control points serve as design variables. Consequently,
these approaches rely on the same design variables as specified by the human
designer. While this choice is convenient, it either restricts the design to
small modifications of given, initial design features - effectively prohibiting
topological changes - or yields undesirably many design variables. In this
work, we step away from CAD and spline-based approaches and construct an
artificial, feature-dense yet low-dimensional optimization space using a
generative neural network. Using the neural network for the geometry
parameterization extends state-of-the-art methods in that the resulting design
space is not restricted to user-prescribed modifications of certain basis
shapes. Instead, within the same optimization space, we can interpolate between
and explore seemingly unrelated designs. To show the performance of this new
approach, we integrate the developed shape parameterization into our numerical
design framework for dynamic mixing elements in plastics extrusion. Finally, we
challenge the novel method in a competitive setting against current free-form
deformation-based approaches and demonstrate the method's performance even at
this early stage.
