연구 자료(Research Data)
1) Repository for segmented point clouds of fitting parts in the process plants for deep learning application
- Number of point clouds: 4,647 segmented point clouds for 17 fitting part types:
699 segmented point clouds for valve-type fittings, 1,620 for the flange series, 15 for the cross series,
507 for the tee series, 18 for olet (branch) types, 75 for strainer types, 300 for the reducer series,
1,341 for the elbow series, and 72 for pipe types
- File type: XYZ format
- Number of points in point clouds: from around 360 points to around 1.4 million points for each segmented point cloud
- File size: from 12 kilobytes to 42 megabytes for each segmented point cloud
For more information on the repository, please refer to the following papers.
- Hyungki Kim, Changmo Yeo, Inhwan Dennis Lee, Duhwan Mun, “Deep-learning-based retrieval of piping component catalogs for plant 3D CAD model reconstruction“, Computers in Industry, Vol. 123, 103320, 2020.12.01.
- Changmo Yeo, Seyoon Kim, Hyungki Kim, Siro Kim, and Duhwan Mun, “Deep learning applications in an industrial process plant: Repository of segmented point clouds for pipework components”, JMST Advances, Vol. 2, No. 1, pp. 15-24, 2020.03.01.
If you want to download the set of segmented point clouds of fitting parts, click here!
2) Dataset for 3D CAD models containing features in OBJ format
- Number of 3D CAD models: 119,320 3D CAD models in two versions:
version 1) 101,000 3D CAD models, two types of features(hole, pocket)
version 2) 18,320 3D CAD models, four types of features(chamfer, fillet, hole, pocket)
- File type: OBJ format
- File size: from 4 kilobytes to 251 kilobytes for each 3D CAD model
For more information on the repository, please refer to the following paper.
- Hyunoh Lee, Jinwon Lee, Hyungki Kim, Duhwan Mun, “Dataset and method for deep learning-based reconstruction of 3D CAD models containing machining features for mechanical parts”, Journal of Computational Design and Engineering, Vol. 9, No. 1, pp. 114-127, 2022.02.01.
If you want to download the set of 3D CAD models containing machining features, click here!
3) Dataset of feature descriptors used for machining feature recognition
- Number of feature descriptors: 2,236 feature descriptors for 17 types
406 descriptors for the hole series, 722 for the slot series, 380 for the pocket series, 356 for the island series,
182 for the fillet series, 46 for the chamfer series, and 174 for the non-feature types
- File type: TXT format
- File size: 122 bytes for each feature descriptor
For more information on the dataset, please refer to the following paper.
- Changmo Yeo, Byung Chul Kim, Sanguk Cheon, Jinwon Lee, and Duhwan Mun, "Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems", Scientific Reports, Vol. 11, 22147, 2021.11.12. (doi: https://doi.org/10.1038/s41598-021-01313-3)
If you want to download the dataset based on feature descriptors, click here!
4) Point clouds used for mesh-model reconstruction tests based on an iterative offset-based method
For more information on the point clouds, please refer to the following paper.
- Kiyoun Kwon, Duhwan Mun, ”Iterative offset-based method for reconstructing a mesh model from the point cloud of a pig”, Computers and Electronics in Agriculture, Vol. 198, 106996, 2022.07.01. (doi: https://doi.org/10.1016/j.compag.2022.106996)
If you want to download the point clouds, click here!
5) Dataset of 3D CAD models used for descriptor-based machining feature recognition
- Number of 3D CAD models: 62 models
- File type: stp file
- File size: from 13 kilobytes to 211 kilobytes for each 3D model
For more information on the dataset, please refer to the following paper.
- Seungeun Lim, Changmo Yeo, Jinwon Lee(Corresponding Author), Duhwan Mun(Corresponding Author), "Machining feature recognition using descriptors with range constraints for mechanical 3D models", arXiv:2301.03167, 2023. (https://doi.org/10.48550/arXiv.2301.03167)
If you want to download the 3D CAD models, click here!