A Dataset for Mechanical Mechanisms
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914939687403520 |
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| author | Ghezelbash, Farshid Eskandari, Amir Hossein Bidhendi, Amir J |
| author_facet | Ghezelbash, Farshid Eskandari, Amir Hossein Bidhendi, Amir J |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03763 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Dataset for Mechanical Mechanisms Ghezelbash, Farshid Eskandari, Amir Hossein Bidhendi, Amir J Computer Vision and Pattern Recognition 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. |
| title | A Dataset for Mechanical Mechanisms |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.03763 |