A Dataset for Mechanical Mechanisms
This work provides a dataset for researchers in mechanical design to leverage generative AI, though it is incremental as it applies existing models to new data.
This paper introduces a dataset of approximately 9,000 images of mechanical mechanisms with descriptions to support mechanism design research, and demonstrates its application by fine-tuning Stable Diffusion for design generation and BLIP-2 for captioning, with results showing promise for 3D sketches but limitations for 2D sketches.
This study introduces a dataset consisting of approximately 9,000 images of mechanical mechanisms and their corresponding descriptions, aimed at supporting research in mechanism design. The dataset consists of a diverse collection of 2D and 3D sketches, meticulously curated to ensure relevance and quality. We demonstrate the application of this dataset by fine-tuning two models: 1) Stable Diffusion (for generating new mechanical designs), and 2) BLIP-2 (for captioning these designs). While the results from Stable Diffusion show promise, particularly in generating coherent 3D sketches, the model struggles with 2D sketches and occasionally produces nonsensical outputs. These limitations underscore the need for further development, particularly in expanding the dataset and refining model architectures. Nonetheless, this work serves as a step towards leveraging generative AI in mechanical design, highlighting both the potential and current limitations of these approaches.