1 citations · 1 across the 1 of their papers we have counts for
4 papers
Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers
Francis Ogoke, Peter Myung-Won Pak, Alexander Myers +4
Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical and fatigue performance.…
Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion
Olabode T. Ajenifujah, Francis Ogoke, Florian Wirth +2
Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. Howeve…
Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion
Francis Ogoke, Quanliang Liu, Olabode Ajenifujah +5
Defects in laser powder bed fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. For instance, the melt pool c…
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…