  Aiming for a higher economic efficiency in manufacturing, an increased degree
of automation is a key enabler. However, assessing the technical feasibility of
an automated assembly solution for a dedicated process is difficult and often
determined by the geometry of the given product parts. Among others, decisive
criterions of the automation feasibility are the ability to separate and
isolate single parts or the capability of component self-alignment in final
position. To assess the feasibility, a questionnaire based evaluation scheme
has been developed and applied by Fraunhofer researchers. However, the results
strongly depend on the implicit knowledge and experience of the single engineer
performing the assessment. This paper presents NeuroCAD, a software tool that
automates the assessment using voxelization techniques. The approach enables
the assessment of abstract and production relevant geometries features through
deep-learning based on CAD files.
