Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)

Scime, Luke; Paquit, Vincent; Joslin, Chase; Richardson, Dylan; Goldsby, Desarae; Lowe, Larry

doi:10.13139/ORNLNCCS/1779073

Title: Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)

Dataset
Other Related Research

Abstract

This dataset contains layer-wise powder bed images from three different powder bed printing technologies â laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.

Authors:

Scime, Luke; Paquit, Vincent; Joslin, Chase; Richardson, Dylan; Goldsby, Desarae; Lowe, Larry

ORNL-OLCF

Publication Date:: Fri Feb 17 04:00:00 UTC 2023

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: 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)

Subject:: 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; Image Segmentation; In-Situ Process Monitoring; Machine Learning; Powder Bed Additive Manufacturing

OSTI Identifier:: 1779073

DOI:: https://doi.org/10.13139/ORNLNCCS/1779073

Citation Formats


                    Scime, Luke, Paquit, Vincent, Joslin, Chase, Richardson, Dylan, Goldsby, Desarae, and Lowe, Larry. Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03).  United States: N. p., 2023. 
        Web.  doi:10.13139/ORNLNCCS/1779073.

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                    Scime, Luke, Paquit, Vincent, Joslin, Chase, Richardson, Dylan, Goldsby, Desarae, & Lowe, Larry. Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03).  United States.  doi:https://doi.org/10.13139/ORNLNCCS/1779073

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                    Scime, Luke, Paquit, Vincent, Joslin, Chase, Richardson, Dylan, Goldsby, Desarae, and Lowe, Larry. 2023.  
"Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)".  United States.  doi:https://doi.org/10.13139/ORNLNCCS/1779073.  https://www.osti.gov/servlets/purl/1779073. Pub date:Fri Feb 17 04:00:00 UTC 2023

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@article{osti_1779073,

  title        = {Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)},

  author       = {Scime, Luke and Paquit, Vincent and Joslin, Chase and Richardson, Dylan and Goldsby, Desarae and Lowe, Larry},

  abstractNote = {This dataset contains layer-wise powder bed images from three different powder bed printing technologies â laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.},

  doi          = {10.13139/ORNLNCCS/1779073},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Feb 17 04:00:00 UTC 2023},

  month        = {Fri Feb 17 04:00:00 UTC 2023}

}

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DOI: https://doi.org/10.13139/ORNLNCCS/1779073

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