Patents by Inventor Desarae J. Goldsby

Desarae J. Goldsby has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • SYSTEMS AND METHODS FOR POWDER BED ADDITIVE MANUFACTURING ANOMALY DETECTION
    Publication number: 20230139949
    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.
    Type: Application
    Filed: October 3, 2022
    Publication date: May 4, 2023
    Inventors: Luke R. Scime, Vincent C. Paquit, Desarae J. Goldsby, William H. Halsey, Chase B. Joslin, Michael D. Richardson, Derek C. Rose, Derek H. Siddel
  • Systems and methods for powder bed additive manufacturing anomaly detection
    Patent number: 11458542
    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.
    Type: Grant
    Filed: November 17, 2020
    Date of Patent: October 4, 2022
    Assignee: UT-Battelle, LLC
    Inventors: Luke R. Scime, Vincent C. Paquit, Desarae J. Goldsby, William H. Halsey, Chase B. Joslin, Michael D. Richardson, Derek C. Rose, Derek H. Siddel
  • SYSTEMS AND METHODS FOR POWDER BED ADDITIVE MANUFACTURING ANOMALY DETECTION
    Publication number: 20220134435
    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.
    Type: Application
    Filed: November 17, 2020
    Publication date: May 5, 2022
    Inventors: Luke R. Scime, Vincent C. Paquit, Desarae J. Goldsby, William H. Halsey, Chase B. Joslin, Michael D. Richardson, Derek C. Rose, Derek H. Siddel