Development of automated visual surface inspection systems heavily depends on the availability of defected product samples. Both inspection hardware configuration and training of defect detection models require diversified, representative and precisely annotated data. Reliable training data of sufficient size is frequently challenging to obtain. Using virtual environments, it is possible to simulate defected products which would serve both for configuration of acquisition hardware as well as for generation of required datasets. In this work, we present parameterized models for adaptable simulation of geometrical defects, based on procedural methods. Presented models are suitable for creating defected products in virtual surface inspection planning environments. As such, they enable inspection planning experts to assess defect visibility for various configurations of acquisition hardware. Finally, the presented method enables pixel-precise annotations alongside image synthesis for the creation of training-ready datasets.