  When it comes to the optimization of CAD models in the automation domain,
neural networks currently play only a minor role. Optimizing abstract features
such as automation capability is challenging, since they can be very difficult
to simulate, are too complex for rule-based systems, and also have little to no
data available for machine-learning methods. On the other hand, image
manipulation methods that can manipulate abstract features in images such as
StyleCLIP have seen much success. They rely on the latent space of pretrained
generative adversarial networks, and could therefore also make use of the vast
amount of unlabeled CAD data. In this paper, we show that such an approach is
also suitable for optimizing abstract automation-related features of CAD parts.
We achieved this by extending StyleCLIP to work with CAD models in the form of
voxel models, which includes using a 3D StyleGAN and a custom classifier.
Finally, we demonstrate the ability of our system for the optimiziation of
automation-related features by optimizing the grabability of various CAD
models. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the
responsibility of the scientific committee of the 33rd CIRP Design Conference.
