公开(公告)号：US20230234137A1

公开(公告)日：2023-07-27

申请号：US18002883

申请日：2021-07-29

Applicant: The Penn State Research Foundation

Inventor： Edward Reutzel ,  Jan Petrich ,  Abdalla R. Nassar ,  Shashi Phoha ,  David J. Corbin ,  Jacob P. Morgan ,  Evan P. Diewald ,  Robert W. Smith ,  Zackary Keller Snow

IPC: B22F10/85 ,  B22F10/38 ,  B22F12/90 ,  B22F10/366

CPC classification number: B22F10/85 ,  B22F10/38 ,  B22F12/90 ,  B22F10/366 ,  B22F2999/00 ,  B33Y40/00

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.