Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)

Scime, Luke; Joslin, Chase; Duncan, Ryan; Brinkley, Frank; Ledford, Christopher; Siddel, Derek; Paquit, Vincent

doi:10.13139/ORNLNCCS/1923043

Title: Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)

Dataset
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Abstract

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, 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 createmore »« less

Authors:

Scime, Luke; Joslin, Chase; Duncan, Ryan; Brinkley, Frank; Ledford, Christopher; Siddel, Derek; Paquit, Vincent

ORNL-OLCF

Publication Date:: Sun Feb 12 23:00:00 EST 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:: 1923043

DOI:: https://doi.org/10.13139/ORNLNCCS/1923043

Citation Formats


                    Scime, Luke, Joslin, Chase, Duncan, Ryan, Brinkley, Frank, Ledford, Christopher, Siddel, Derek, and Paquit, Vincent. Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1).  United States: N. p., 2023. 
        Web.  doi:10.13139/ORNLNCCS/1923043.

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                    Scime, Luke, Joslin, Chase, Duncan, Ryan, Brinkley, Frank, Ledford, Christopher, Siddel, Derek, & Paquit, Vincent. Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1).  United States.  doi:https://doi.org/10.13139/ORNLNCCS/1923043

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                    Scime, Luke, Joslin, Chase, Duncan, Ryan, Brinkley, Frank, Ledford, Christopher, Siddel, Derek, and Paquit, Vincent. 2023.  
"Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)".  United States.  doi:https://doi.org/10.13139/ORNLNCCS/1923043.  https://www.osti.gov/servlets/purl/1923043. Pub date:Sun Feb 12 23:00:00 EST 2023

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@article{osti_1923043,

  title        = {Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)},

  author       = {Scime, Luke and Joslin, Chase and Duncan, Ryan and Brinkley, Frank and Ledford, Christopher and Siddel, Derek and Paquit, Vincent},

  abstractNote = {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, 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.},

  doi          = {10.13139/ORNLNCCS/1923043},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Feb 12 23:00:00 EST 2023},

  month        = {Sun Feb 12 23:00:00 EST 2023}

}

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DOI: https://doi.org/10.13139/ORNLNCCS/1923043

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