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High-Fidelity Measurements of Spatter Sizes, Shapes, and Landing Sites in a Laser Powder Bed Fusion Machine

Published by National Institute of Standards and Technology | National Institute of Standards and Technology | Catalog Last Checked: September 02, 2026 at 07:19 PM | Dataset Last Updated: July 27, 2026
Spatter particles from nickel superalloy 625 were captured and measured during the printing of a 25 mm x 10 mm rectangular pad in commercial laser powder bed fusion (PBF-LB) machine. Particles were captured with a glue-coated sticky plate that covered the entire powder bed, holding any landed particles at their original landing sites for ex-situ measurement in an optical microscope. Large-area images of the sticky plates were processed with a convolutional neural network (CNN) to automatically identify spatter particles. The landing positions, ferret diameters, and circularities of identified particles were then measured using a blob detection algorithm. This dataset includes the original images from the microscope, the labeled dataset for training and testing the CNN, the CNN-segmented images (identifying spatter particles), the final measurements of detected spatter particles, and several relevant scripts and figures to describe the experiment. Certain commercial equipment, instruments, or materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

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Complete Metadata
@type dcat:Dataset
accessLevel public
accrualPeriodicity irregular
bureauCode 
[
  "006:55"
]
contactPoint 
{
  "fn": "Nicholas O'Brien",
  "hasEmail": "mailto:nicholas.obrien@nist.gov"
}
description Spatter particles from nickel superalloy 625 were captured and measured during the printing of a 25 mm x 10 mm rectangular pad in commercial laser powder bed fusion (PBF-LB) machine. Particles were captured with a glue-coated sticky plate that covered the entire powder bed, holding any landed particles at their original landing sites for ex-situ measurement in an optical microscope. Large-area images of the sticky plates were processed with a convolutional neural network (CNN) to automatically identify spatter particles. The landing positions, ferret diameters, and circularities of identified particles were then measured using a blob detection algorithm. This dataset includes the original images from the microscope, the labeled dataset for training and testing the CNN, the CNN-segmented images (identifying spatter particles), the final measurements of detected spatter particles, and several relevant scripts and figures to describe the experiment. Certain commercial equipment, instruments, or materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.
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identifier ark:/88434/mds2-4277
issued 2026-08-26
keyword 
[
  "EOS M290",
  "additive manufacturing",
  "computer vision",
  "convolutional neural network",
  "in-process monitoring",
  "in-situ monitoring",
  "inconel 625",
  "inconel 718",
  "laser powder bed fusion",
  "machine learning",
  "particle detection",
  "particle measurement",
  "spatter contamination",
  "spatter particles"
]
landingPage https://data.nist.gov/od/id/mds2-4277
language 
[
  "en"
]
license https://www.nist.gov/open/license
modified 2026-07-27 00:00:00
programCode 
[
  "006:045"
]
publisher 
{
  "name": "National Institute of Standards and Technology",
  "@type": "org:Organization"
}
references 
[
  "https://doi.org/10.1016/j.addma.2026.105303"
]
theme 
[
  "Manufacturing:Additive manufacturing",
  "Manufacturing:Monitoring, diagnostics and prognostics"
]
title High-Fidelity Measurements of Spatter Sizes, Shapes, and Landing Sites in a Laser Powder Bed Fusion Machine

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