  Adapting robot programmes to changes in the environment is a well-known
industry problem, and it is the reason why many tedious tasks are not automated
in small and medium-sized enterprises (SMEs). A semantic world model of a
robot's previously unknown environment created from point clouds is one way for
these companies to automate assembly tasks that are typically performed by
humans. The semantic segmentation of point clouds for robot manipulators or
cobots in industrial environments has received little attention due to a lack
of suitable datasets. This paper describes a pipeline for creating synthetic
point clouds for specific use cases in order to train a model for point cloud
semantic segmentation. We show that models trained with our data achieve high
per-class accuracy (> 90%) for semantic point cloud segmentation on unseen
real-world data. Our approach is applicable not only to the 3D camera used in
training data generation but also to other depth cameras based on different
technologies. The application tested in this work is a industry-related
peg-in-the-hole process. With our approach the necessity of user assistance
during a robot's commissioning can be reduced to a minimum.
