3 citations · 5 across the 4 of their papers we have counts for
5 papers
Microstructural Generative Inverse Models for Additive Manufacturing Digital Twins
Pablo Luna Falcon, Jason Mayeur, Amirkoushyar Ziabari +2
Metal additive manufacturing (AM) offers substantial advantages to the fabrication of complex structural components, but its widespread adoption is slowed due to complexities in th…
Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation
Gunnika Kapoor, Komal Chawla, Tirthankar Ghosal +6
Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materia…
Deep learning based workflow for accelerated industrial X-ray Computed Tomography
Obaidullah Rahman, Singanallur V. Venkatakrishnan, Luke Scime +5
X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal…
Deep-Learning Quantitative Structural Characterization in Additive Manufacturing
Amra Peles, Vincent C. Paquit, Ryan R. Dehoff
With a goal of accelerating fabrication of additively manufactured components with precise microstructures, we developed a method for structural characterization of key features in…
X-Ray CT Reconstruction of Additively Manufactured Parts using 2.5D Deep Learning MBIR
Amirkoushyar Ziabari, Michael Kirka, Vincent Paquit +2
In this paper, we present a deep learning algorithm to rapidly obtain high quality CT reconstructions for AM parts. In particular, we propose to use CAD models of the parts that ar…