The Penn State Research Foundation

Louisiana Tech University

Shashi Phoha

Vir V Phoha

Abdalla R Nassar

Edward W Reutzel

Asok Ray

Robert W Smith

Shrijit Sudhakar Joshi

Evan P Diewald

Zackary Keller Snow

Sampath Kumar Vuyyuru

David J Corbin

Jan Petrich

Jacob P Morgan

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.

In-situ process monitoring for powder bed fusion additive manufacturing (PBF AM) processes using multi-modal sensor fusion machine learning

Hidden Markov Models ('HMMs') are used to analyze keystroke dynamics measurements collected as a user types a predetermined string on a keyboard. A user enrolls by typing the predetermined string several times; the enrollment samples are used to train a HMM to identify the user. A candidate who claims to be the user provides a typing sample, and the HMM produces a probability to estimate the likelihood that the candidate is the user he claims to be. A computationally-efficient method for preparing HMMs to analyze certain types of processes is also described.

Hidden markov model ('HMM')-based user authentication using keystroke dynamics

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Shashi Phoha