Patents by Inventor David J. Corbin

David J. Corbin 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).

  • IN-SITU PROCESS MONITORING FOR POWDER BED FUSION ADDITIVE MANUFACTURING (PBF AM) PROCESSES USING MULTI-MODAL SENSOR FUSION MACHINE LEARNING
    Publication number: 20250339899
    Abstract: Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.
    Type: Application
    Filed: July 10, 2025
    Publication date: November 6, 2025
    Inventors: Edward Reutzel, Jan Petrich, Abdalla R. Nassar, Shashi Phoha, David J. Corbin, Jacob P. Morgan, Evan P. Diewald, Robert W. Smith, Zackary Keller Snow
  • In-situ process monitoring for powder bed fusion additive manufacturing (PBF AM) processes using multi-modal sensor fusion machine learning
    Patent number: 12383960
    Abstract: Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.
    Type: Grant
    Filed: July 29, 2021
    Date of Patent: August 12, 2025
    Assignee: THE PENN STATE RESEARCH FOUNDATION
    Inventors: Edward Reutzel, Jan Petrich, Abdalla R. Nassar, Shashi Phoha, David J. Corbin, Jacob P. Morgan, Evan P. Diewald, Robert W. Smith, Zackary Keller Snow
  • IN-SITU PROCESS MONITORING FOR POWDER BED FUSION ADDITIVE MANUFACTURING (PBF AM) PROCESSES USING MULTI-MODAL SENSOR FUSION MACHINE LEARNING
    Publication number: 20230234137
    Abstract: Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.
    Type: Application
    Filed: July 29, 2021
    Publication date: July 27, 2023
    Inventors: Edward Reutzel, Jan Petrich, Abdalla R. Nassar, Shashi Phoha, David J. Corbin, Jacob P. Morgan, Evan P. Diewald, Robert W. Smith, Zackary Keller Snow