Garcia-Moreno, Francisco

DOE OSTI2024

Systems and methods for additive manufacturing magnetic solenoids

Systems and methods for forming a magnetically-enabled part via additive manufacturing. The method includes depositing a layer of additive manufacturing material on a build plate, melting or sintering the layer of additive manufacturing material, depositing additional layers of additive manufacturing material on previous layers of additive manufacturing material, the additive manufacturing material of at least some of the additional layers being magnetically permeable, and melting or sintering the additional layers of additive manufacturing material such that the magnetically-enabled part has a transition region including at least some of the magnetically permeable additive manufacturing material.

Hatch, Jonathan Douglas↗

DOE OSTI2023

Systems and methods for additive manufacturing magnetic solenoids

Systems and methods for forming a magnetically-enabled part via additive manufacturing. The method includes depositing a layer of additive manufacturing material on a build plate, melting or sintering the layer of additive manufacturing material, depositing additional layers of additive manufacturing material on previous layers of additive manufacturing material, the additive manufacturing material of at least some of the additional layers being magnetically permeable, and melting or sintering the additional layers of additive manufacturing material such that the magnetically-enabled part has a transition region including at least some of the magnetically permeable additive manufacturing material.

Hatch, Jonathan Douglas↗

DOE OSTI2021

PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets

Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase information from an intensity hologram or diffraction pattern in a robust manner and in real-time. However, current DL architectures applied to the phase problem rely on i) paired datasets, i. e., they arc only applicable when a satisfactory solution of the phase problem has been found, and ii) the fact that most of them ignore the physics of the imaging process. Here, we present PhaseGAN, a new DL approach based on Generative Adversarial Networks, which allows the use of unpaired datasets and includes the physics of image formation. The performance of our approach is enhanced by including the image formation physics and a novel Fourier loss function, providing phase reconstructions when conventional phase retrieval algorithms fail, such as ultra-fast experiments. Thus, PhaseGAN offers the opportunity to address the phase problem in real-time when no phase reconstructions but good simulations or data from other experiments are available.

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