11 citations · 14 across the 7 of their papers we have counts for
8 papers
Airborne acoustic emission enables sub-scanline keyhole porosity quantification and effective process characterization for metallic laser powder bed fusion
Haolin Liu, David Guirguis, Xuzhe Zeng +7
Keyhole-induced (KH) porosity, which arises from unstable vapor cavity dynamics under excessive laser energy input, remains a significant challenge in laser powder bed fusion (LPBF…
Equilibrium Conserving Neural Operators for Super-Resolution Learning
Vivek Oommen, Andreas E. Robertson, Daniel Diaz +5
Neural surrogate solvers can estimate solutions to partial differential equations in physical problems more efficiently than standard numerical methods, but require extensive high-…
Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations
Jiahui Zhang, Runbo Jiang, Kangming Li +11
The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced d…
Inference of highly time-resolved melt pool visual characteristics and spatially-dependent lack-of-fusion defects in laser powder bed fusion using acoustic and thermal emission data
Haolin Liu, Christian Gobert, Kevin Ferguson +5
With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers t…
Three-Dimensional In Situ Texture Development and Plasticity Accumulation in the Cyclic Loading of an alpha-Ti Alloy
Rachel E. Lim, Joel V. Bernier, Darren C. Pagan +2
High-energy synchrotron x-rays are used to track grain rotations and the micromechanical evolution of a hexagonal Ti-7Al microstructure as it is cyclically loaded below its macrosc…
Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
Tim Hsu, William K. Epting, Hokon Kim +5
Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of…