66 citations · 133 across the 12 of their papers we have counts for
12 papers
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…
AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing
Achuth Chandrasekhar, Jonathan Chan, Francis Ogoke +2
Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-leve…
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…
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…
Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
Zijie Li, Saurabh Patil, Francis Ogoke +5
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs). Different from many existing n…
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…
Surrogate Modeling of Melt Pool Thermal Field using Deep Learning
AmirPouya Hemmasian, Francis Ogoke, Parand Akbari +3
Powder-based additive manufacturing has transformed the manufacturing industry over the last decade. In Laser Powder Bed Fusion, a specific part is built in an iterative manner in…
Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing
Francis Ogoke, Kyle Johnson, Michael Glinsky +3
Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. Howe…
MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning
Parand Akbari, Francis Ogoke, Ning-Yu Kao +4
Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on…
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning
Francis Ogoke, Amir Barati Farimani
Powder-based additive manufacturing techniques provide tools to construct intricate structures that are difficult to manufacture using conventional methods. In Laser Powder Bed Fus…
Graph Convolutional Neural Networks for Body Force Prediction
Francis Ogoke, Kazem Meidani, Amirreza Hashemi +1
Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order o…