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.

翻译：在自动化领域的计算机辅助设计（CAD）模型优化中，神经网络目前仅发挥次要作用。优化诸如自动化能力这类抽象特征极具挑战性，因其既难以仿真模拟，又对基于规则的系统过于复杂，且缺乏可供机器学习方法使用的数据。另一方面，图像操控方法（如StyleCLIP）在操控图像中的抽象特征方面已取得显著成功。这类方法依赖于预训练生成对抗网络的潜空间，因此也可利用海量未标注的CAD数据。本文证明，此类方法同样适用于优化CAD零件中与自动化相关的抽象特征。我们通过将StyleCLIP扩展至以体素模型形式处理的CAD模型来实现这一目标，具体包括采用3D StyleGAN和定制分类器。最后，通过优化多种CAD模型的可抓取性，展示了本系统在自动化相关特征优化方面的能力。本文为开放获取文章，遵循CC BY-NC-ND许可协议（http://creativecommons.org/licenses/by-nc-nd/4.0/），由第33届CIRP设计会议科学委员会负责同行评审。