CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designs
<|reference_start|>CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designs: Text-to-image generative models have increasingly been used to assist designers during concept generation in various creative domains, such as graphic design, user interface design, and fashion design. However, their applications in engineering design remain limited due to the models' challenges in generating images of feasible designs concepts. To address this issue, this paper introduces a method that improves the design feasibility by prompting the generation with feasible CAD images. In this work, the usefulness of this method is investigated through a case study with a bike design task using an off-the-shelf text-to-image model, Stable Diffusion 2.1. A diverse set of bike designs are produced in seven different generation settings with varying CAD image prompting weights, and these designs are evaluated on their perceived feasibility and novelty. Results demonstrate that the CAD image prompting successfully helps text-to-image models like Stable Diffusion 2.1 create visibly more feasible design images. While a general tradeoff is observed between feasibility and novelty, when the prompting weight is kept low around 0.35, the design feasibility is significantly improved while its novelty remains on par with those generated by text prompts alone. The insights from this case study offer some guidelines for selecting the appropriate CAD image prompting weight for different stages of the engineering design process. When utilized effectively, our CAD image prompting method opens doors to a wider range of applications of text-to-image models in engineering design.<|reference_end|>
@article{chong2024cad-prompted,
    title={CAD-Prompted Generative Models: A Pathway to Feasible and Novel
  Engineering Designs},
    author={Leah Chong, Jude Rayan, Steven Dow, Ioanna Lykourentzou, Faez Ahmed},
    journal={arXiv preprint arXiv:2407.08675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08675},
    primaryClass={cs.AI}
}