5 papers
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Cosmin Safta, Reese E. Jones, Ravi G. Patel +4
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differ…
Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring
Matthew McKinney, Anthony Garland, Dale Cillessen +5
Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments generate vast amounts of mu…
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring
Francis Ogoke, Sumesh Kalambettu Suresh, Jesse Adamczyk +4
The stochastic formation of defects during Laser Powder Bed Fusion (L-PBF) negatively impacts its adoption for high-precision use cases. Optical monitoring techniques can be used t…
Multiscale simulation of spatially correlated microstructure via a latent space representation
Reese E. Jones, Craig M. Hamel, Dan Bolintineanu +5
When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be…
ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning
Peter Myung-Won Pak, Francis Ogoke, Andrew Polonsky +5
We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring dat…