Optimizing CAD Models with Latent Space Manipulation
This work addresses the problem of automating CAD model optimization for engineers, but it is incremental as it extends existing image manipulation methods to a new domain.
The paper tackled the challenge of optimizing abstract automation-related features in CAD models, which are difficult to simulate or learn from data, by adapting StyleCLIP to work with 3D voxel models using a 3D StyleGAN and custom classifier, and demonstrated its effectiveness by improving the grabability of various CAD models.
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.