A Dataset for Mechanical Mechanisms
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 q...
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creator | Ghezelbash, Farshid Eskandari, Amir Hossein Bidhendi, Amir J |
description | 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. |
doi_str_mv | 10.48550/arxiv.2409.03763 |
format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2409.03763</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2024-08</creationdate><rights>http://creativecommons.org/publicdomain/zero/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,886</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2409.03763$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2409.03763$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ghezelbash, Farshid</creatorcontrib><creatorcontrib>Eskandari, Amir Hossein</creatorcontrib><creatorcontrib>Bidhendi, Amir J</creatorcontrib><title>A Dataset for Mechanical Mechanisms</title><description>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.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjGw1DMwNjcz5mRQdlRwSSxJLE4tUUjLL1LwTU3OSMzLTE7MgTGLc4t5GFjTEnOKU3mhNDeDvJtriLOHLti4-IKizNzEosp4kLHxYGONCasAAFd5K3g</recordid><startdate>20240819</startdate><enddate>20240819</enddate><creator>Ghezelbash, Farshid</creator><creator>Eskandari, Amir Hossein</creator><creator>Bidhendi, Amir J</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240819</creationdate><title>A Dataset for Mechanical Mechanisms</title><author>Ghezelbash, Farshid ; Eskandari, Amir Hossein ; Bidhendi, Amir J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2409_037633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Ghezelbash, Farshid</creatorcontrib><creatorcontrib>Eskandari, Amir Hossein</creatorcontrib><creatorcontrib>Bidhendi, Amir J</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ghezelbash, Farshid</au><au>Eskandari, Amir Hossein</au><au>Bidhendi, Amir J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Dataset for Mechanical Mechanisms</atitle><date>2024-08-19</date><risdate>2024</risdate><abstract>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.</abstract><doi>10.48550/arxiv.2409.03763</doi><oa>free_for_read</oa></addata></record> |
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title | A Dataset for Mechanical Mechanisms |
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