Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)
10.13139/ORNLNCCS/1923043This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, GammaPrint-700, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR). To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Common troubleshooting steps: (a) Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (b) Create an exception for Globus in your antivirus software so that it can create an Endpoint. (c) Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.
Published: 2023-02-13 16:15:14 Download DatasetDataset Properties
Field | Value |
---|---|
Authors |
|
Project Identifier | Peregrine |
Dataset Type | SM Specialized Mix |
Subjects |
|
Keywords |
|
Originating Organizations | Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) |
Sponsoring Organizations | Office of Energy Efficiency and Renewable Energy (EERE);Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A);Office of Nuclear Energy (NE) |
DOE Contract | DE-AC05-00OR22725 |
Related Identifiers |
|
Acknowledgements
Papers using this dataset are requested to include the following text in their acknowledgements:
*Support for 10.13139/ORNLNCCS/1923043 is provided by the U.S. Department of Energy, project Peregrine under Contract DE-AC05-00OR22725. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility.