Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures
This document describes the collection and organization of a dataset that consists of raw videos and extracted sub-images from video frames of a promising additive manufacturing technique called Two-Photon Lithography (TPL). Four unprocessed videos were collected, with each video capturing the prin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-10-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340920310131 |
| _version_ | 1818881969264525312 |
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| author | Xian Yeow Lee Sourabh K. Saha Soumik Sarkar Brian Giera |
| author_facet | Xian Yeow Lee Sourabh K. Saha Soumik Sarkar Brian Giera |
| author_sort | Xian Yeow Lee |
| collection | DOAJ |
| description | This document describes the collection and organization of a dataset that consists of raw videos and extracted sub-images from video frames of a promising additive manufacturing technique called Two-Photon Lithography (TPL). Four unprocessed videos were collected, with each video capturing the printing process of different combinations of 3D parts on different photoresists at varying light dosages. These videos were further trimmed to obtain short clips that are organized by experimental parameters. Additionally, this dataset also contains a python script to reproduce an organized directory of cropped video frames extracted from the trimmed videos. These cropped frames focus on a region of interest around the parts being printed. We envision that the raw videos and cropped frames provided in this dataset will be used to train various computer vision and machine learning algorithms for applications such as object segmentation and localization applications. The cropped video frames were manually labelled by an expert to determine the quality of the printed part and for printing parameter optimization as presented in “Automated Detection of Part Quality during Two-Photon Lithography via Deep Learning” [1]. |
| first_indexed | 2024-12-19T15:10:18Z |
| format | Article |
| id | doaj.art-2fbedd23b9b744ddac2f765c90cc651e |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-19T15:10:18Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-2fbedd23b9b744ddac2f765c90cc651e2022-12-21T20:16:19ZengElsevierData in Brief2352-34092020-10-0132106119Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structuresXian Yeow Lee0Sourabh K. Saha1Soumik Sarkar2Brian Giera3Department of Mechanical Engineering, Iowa State University, United StatesG.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, United StatesDepartment of Mechanical Engineering, Iowa State University, United StatesLawrence Livermore National Laboratory, United States; Corresponding author.This document describes the collection and organization of a dataset that consists of raw videos and extracted sub-images from video frames of a promising additive manufacturing technique called Two-Photon Lithography (TPL). Four unprocessed videos were collected, with each video capturing the printing process of different combinations of 3D parts on different photoresists at varying light dosages. These videos were further trimmed to obtain short clips that are organized by experimental parameters. Additionally, this dataset also contains a python script to reproduce an organized directory of cropped video frames extracted from the trimmed videos. These cropped frames focus on a region of interest around the parts being printed. We envision that the raw videos and cropped frames provided in this dataset will be used to train various computer vision and machine learning algorithms for applications such as object segmentation and localization applications. The cropped video frames were manually labelled by an expert to determine the quality of the printed part and for printing parameter optimization as presented in “Automated Detection of Part Quality during Two-Photon Lithography via Deep Learning” [1].http://www.sciencedirect.com/science/article/pii/S2352340920310131Additive manufacturingMachine learningProcess monitoringMulti-photon polymerizationDirect laser writing |
| spellingShingle | Xian Yeow Lee Sourabh K. Saha Soumik Sarkar Brian Giera Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures Data in Brief Additive manufacturing Machine learning Process monitoring Multi-photon polymerization Direct laser writing |
| title | Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures |
| title_full | Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures |
| title_fullStr | Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures |
| title_full_unstemmed | Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures |
| title_short | Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures |
| title_sort | two photon lithography additive manufacturing video dataset of parameter sweep of light dosages photo curable resins and structures |
| topic | Additive manufacturing Machine learning Process monitoring Multi-photon polymerization Direct laser writing |
| url | http://www.sciencedirect.com/science/article/pii/S2352340920310131 |
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