Data provided by National Institute of Standards and Technology
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.
About this Dataset
Updated: 2026-09-19
Metadata Last Updated:
2026-07-27 00:00:00
Date Created:
N/A
Data Provided by:
Dataset Owner:
N/A
| Title | High-Fidelity Measurements of Spatter Sizes, Shapes, and Landing Sites in a Laser Powder Bed Fusion Machine |
|---|---|
| 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. |
| Modified | 2026-07-27 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | machine learning , convolutional neural network , particle measurement , particle detection , computer vision , laser powder bed fusion , additive manufacturing , inconel 718 , inconel 625 , spatter contamination , spatter particles , EOS M290 , in-process monitoring , in-situ monitoring |
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"title": "High-Fidelity Measurements of Spatter Sizes, Shapes, and Landing Sites in a Laser Powder Bed Fusion Machine",
"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. \n\nCertain 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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"name": "National Institute of Standards and Technology"
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"theme": [
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"Manufacturing:Additive manufacturing"
],
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"convolutional neural network",
"particle measurement",
"particle detection",
"computer vision",
"laser powder bed fusion",
"additive manufacturing",
"inconel 718",
"inconel 625",
"spatter contamination",
"spatter particles",
"EOS M290",
"in-process monitoring",
"in-situ monitoring"
]
}
