Systems and methods for powder bed additive manufacturing anomaly detection
Abstract
Detection and classification of anomalies for powder bed metal additive manufacturing. Anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each layer of the building process. A multi-scaled parallel dynamic segmentation convolutional neural network architecture provides additive manufacturing machine and imaging system agnostic pixel-wise semantic segmentation of layer-wise powder bed image data. Learned knowledge is easily transferrable between different additive manufacturing machines. The anomaly detection can be conducted in real-time and provides accurate and generalizable results.
- Inventors:
- Issue Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1986640
- Patent Number(s):
- 11458542
- Application Number:
- 16/950,484
- Assignee:
- UT-Battelle LLC (Oak Ridge, TN)
- Patent Classifications (CPCs):
-
B - PERFORMING OPERATIONS B22 - CASTING B22F - WORKING METALLIC POWDER
B - PERFORMING OPERATIONS B29 - WORKING OF PLASTICS B29C - SHAPING OR JOINING OF PLASTICS
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 11/17/2020
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Scime, Luke R., Paquit, Vincent C., Goldsby, Desarae J., Halsey, William H., Joslin, Chase B., Richardson, Michael D., Rose, Derek C., and Siddel, Derek H. Systems and methods for powder bed additive manufacturing anomaly detection. United States: N. p., 2022.
Web.
Scime, Luke R., Paquit, Vincent C., Goldsby, Desarae J., Halsey, William H., Joslin, Chase B., Richardson, Michael D., Rose, Derek C., & Siddel, Derek H. Systems and methods for powder bed additive manufacturing anomaly detection. United States.
Scime, Luke R., Paquit, Vincent C., Goldsby, Desarae J., Halsey, William H., Joslin, Chase B., Richardson, Michael D., Rose, Derek C., and Siddel, Derek H. Tue .
"Systems and methods for powder bed additive manufacturing anomaly detection". United States. https://www.osti.gov/servlets/purl/1986640.
@article{osti_1986640,
title = {Systems and methods for powder bed additive manufacturing anomaly detection},
author = {Scime, Luke R. and Paquit, Vincent C. and Goldsby, Desarae J. and Halsey, William H. and Joslin, Chase B. and Richardson, Michael D. and Rose, Derek C. and Siddel, Derek H.},
abstractNote = {Detection and classification of anomalies for powder bed metal additive manufacturing. Anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each layer of the building process. A multi-scaled parallel dynamic segmentation convolutional neural network architecture provides additive manufacturing machine and imaging system agnostic pixel-wise semantic segmentation of layer-wise powder bed image data. Learned knowledge is easily transferrable between different additive manufacturing machines. The anomaly detection can be conducted in real-time and provides accurate and generalizable results.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {10}
}
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