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Title: 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}
}

Works referenced in this record:

Multi-scale Convolutional Neural Networks for Lung Nodule Classification
book, January 2015


Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion
journal, December 2018


Vision-Based Inspection System for Dimensional Accuracy in Powder-Bed Additive Manufacturing
conference, June 2016


Detection of elevated regions in surface images from laser beam melting processes
conference, November 2015


Flaw detection in powder bed fusion using optical imaging
journal, May 2017


DeepEdge: A multi-scale bifurcated deep network for top-down contour detection
conference, June 2015