5 datasets found
  1. o

    Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes...

    • osti.gov
    Updated Feb 17, 2023
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    Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF) (2023). Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10) [Dataset]. http://doi.org/10.13139/ORNLNCCS/1896716
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    Dataset updated
    Feb 17, 2023
    Dataset provided by
    Office of Energy Efficiency and Renewable Energy (EERE)
    Office of Nuclear Energy (NE)
    Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
    Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A)
    Description

    This 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, DMREF, 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: 1. Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. 2. Create an exception for Globus in your antivirus software so that it can create an Endpoint. 3. Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.

  2. o

    A Co-Registered In-Situ and Ex-Situ Dataset from an Electron Beam Powder Bed...

    • osti.gov
    Updated Sep 28, 2023
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    Office of Energy Efficiency and Renewable Energy (EERE) (2023). A Co-Registered In-Situ and Ex-Situ Dataset from an Electron Beam Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-09) [Dataset]. http://doi.org/10.13139/ORNLNCCS/2001412
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    Dataset updated
    Sep 28, 2023
    Dataset provided by
    Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
    Office of Energy Efficiency and Renewable Energy (EERE)
    Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A)
    Description

    This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Arcam Q10 Electron Beam Powder Bed Fusion (EB-PBF) Inconel 738 build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise Near Infrared (NIR) in-situ imaging data, in-situ temporal sensor data, ex-situ X-Ray Computed Tomography (X-CT) scans, and the target part geometries. Additionally, anomaly detections produced by a trained Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided. To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. You may need to create an exception for Globus in your antivirus software so that it can create an Endpoint. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Be sure to confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (4) Sometimes users will need to manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab before the download will begin.

  3. F

    µCT data of additively manufactured downskin surface test specimens...

    • data.uni-hannover.de
    • service.tib.eu
    .zip, png
    Updated Sep 22, 2023
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    Institut für Produktentwicklung und Gerätebau (2023). µCT data of additively manufactured downskin surface test specimens according to DIN EN ISO/ASTM 52902 for 316L and CuCrZr [Dataset]. https://data.uni-hannover.de/lv/dataset/415667ec-e70b-46c3-b815-ce3a058203a2
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    png(1671772), .zip(39854108), .zip(305788882)Available download formats
    Dataset updated
    Sep 22, 2023
    Dataset authored and provided by
    Institut für Produktentwicklung und Gerätebau
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    This dataset contains micro-computed tomography images of selected additively manufactured (powder bed fusion of metals using a laser beam, PBF-LB/M) downskin surface test specimens in accordance with DIN EN ISO/ASTM 52902:2020-05. Individual scans of flat tabs with downskin angles of 15° were acquired per material to ensure comparability.

    • Materials: 316L (Carpenter Additive) and CuCrZr (Eckart TLS GmbH)
    • Downskin Angle: 15°
    • PBF-LB Machine: Aconity MIDI+ equipped with an integrated Aerosint SPD Recoater V1.0
    • µCT Scanner: Bruker SkyScan 1275

    https://data.uni-hannover.de/dataset/415667ec-e70b-46c3-b815-ce3a058203a2/resource/09c26a6e-b898-4f6d-9ef4-203771c7f315/download/downskin_52902_316l_15deg_volumetric.png" alt="Exemplary volumetric representation of the 316L 15° test specimen"> Exemplary volumetric representation of the 316L 15° test specimen

    References

    The acquired data was used as part of the following publications, which can be referenced for further details:

    • Meyer, I., Oel, M., Ehlers, T., & Lachmayer, R. (2023). Additive manufacturing of multi-material parts – Design guidelines for manufacturing of 316L/CuCrZr in laser powder bed fusion. Heliyon, 9(8), e18301. https://doi.org/10.1016/j.heliyon.2023.e18301
    • Meyer, I. (2022). Belichtungsprozess für die Multimaterialfertigung von Testkörpern im Laser powder bed fusion. https://doi.org/10.5446/58540

    Acknowledgments

    • This research has been funded by the Ministry for Science and Culture of Lower Saxony (MWK) – School for Additive Manufacturing SAM.
    • The project “Major Research Instrumentation for integration of efficient effects in multi-material structural components” was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project number 445707542.
    • The project “Computer tomograph for optomechatronic systems” was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project number 432176896.
  4. r

    In Process Porosity Detection Dataset by Means of Optical Measurement...

    • radar-service.eu
    • radar.kit.edu
    tar
    Updated Jun 24, 2023
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    Jork Groenewold; Gisela Lanza; Lukas Weiser (2023). In Process Porosity Detection Dataset by Means of Optical Measurement Technology in PBF-LB/M by wbk Institute of Production Science [Dataset]. http://doi.org/10.35097/1571
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    tar(82003470336 bytes)Available download formats
    Dataset updated
    Jun 24, 2023
    Dataset provided by
    Lanza, Gisela
    Groenewold, Jork
    Karlsruhe Institute of Technology
    Authors
    Jork Groenewold; Gisela Lanza; Lukas Weiser
    Description

    In-Process Porosity Detection Dataset by Means of Optical Measurement Technology in PBF-LB/M

    Please see provided Jupyter Notebook "README" for further information.

  5. r

    In Process Porosity Detection Dataset by Means of Structure-Borne Acoustic...

    • radar-service.eu
    • radar.kit.edu
    tar
    Updated Jun 22, 2023
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    Jork Groenewold; Christian Platt; Lukas Weiser; Nicolas Schwartz; Gisela Lanza (2023). In Process Porosity Detection Dataset by Means of Structure-Borne Acoustic Emission Measurement Technology in PBF-LB/M by wbk Institute of Production Science and ZEISS - Part 2 [Dataset]. http://doi.org/10.35097/1449
    Explore at:
    tar(178944220672 bytes)Available download formats
    Dataset updated
    Jun 22, 2023
    Dataset provided by
    Platt, Christian
    Schwartz, Nicolas
    Lanza, Gisela
    Groenewold, Jork
    Karlsruhe Institute of Technology
    Authors
    Jork Groenewold; Christian Platt; Lukas Weiser; Nicolas Schwartz; Gisela Lanza
    Description

    In-Process Porosity Detection Dataset by Means of Structure-Borne Acoustic Emission Measurement Technology in PBF-LB/M by wbk Institute of Production Science and ZEISS

    Please see provided Jupyter Notebook "README" for further information. This dataset is part 2 of 2.

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Click to copy link
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Close
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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF) (2023). Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10) [Dataset]. http://doi.org/10.13139/ORNLNCCS/1896716

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)

Explore at:
10 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 17, 2023
Dataset provided by
Office of Energy Efficiency and Renewable Energy (EERE)
Office of Nuclear Energy (NE)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A)
Description

This 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, DMREF, 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: 1. Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. 2. Create an exception for Globus in your antivirus software so that it can create an Endpoint. 3. Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.

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