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Search Publications by: Jordan Weaver (Fed)

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Displaying 1 - 19 of 19

AM Bench 2022 Macroscale Tensile Challenge at Different Orientations (CHAL-AMB2022-04-MaTTO) and Summary of Predictions

January 16, 2024
Author(s)
Newell Moser, Jake Benzing, Orion Kafka, Jordan Weaver, Nicholas Derimow, Ross Rentz, Nik Hrabe
The additive manufacturing benchmarking challenge described in this work was aimed at the prediction of average stress–strain properties for tensile specimens that were excised from blocks of non-heat-treated IN625 manufactured by laser powder bed fusion

Micromechanical Properties of Spherical and Facetted He Bubble Loaded Copper

June 1, 2023
Author(s)
Osman El Atwani, Jonathan Gigax, Rodney McCabe, Demircan Canadinc, Matthew Chancey, Jordan Weaver
Exploring new irradiation resistant materials requires understanding their mechanical responses to irradiation. Resistance to helium bubble formation and understanding bubble effects on the mechanical response of candidate materials are crucial factors to

Laser spot size and scaling laws for laser beam additive manufacturing

November 3, 2021
Author(s)
Jordan Weaver, Jarred C. Heigel, Brandon Lane
Laser powder bed fusion (L-PBF) additive manufacturing (AM) requires the careful selection of laser process parameters for each feedstock material and machine, which is a laborious process. Scaling laws based on the laser power, speed, and spot size; melt

Inert Gas Flow Speed Measurements in Laser Powder Bed Fusion Additive Manufacturing

October 19, 2021
Author(s)
Jordan Weaver, Alec Schlenoff, David Deisenroth, Shawn P. Moylan
Laser powder bed fusion (LPBF) is an additive manufacturing technology that uses a laser to selectively melt powder feedstock to build parts in a layer-by-layer process. For metals-based LBPF additive manufacturing, the interaction of the laser and powder

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization

August 3, 2021
Author(s)
Jordan Weaver, Adam L. Pintar, Carlos R. Beauchamp, Howard Joress, Kil-Won Moon, Thien Q. Phan
High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing

The effects of particle size distribution on the rheological properties of the powder and the mechanical properties of additively manufactured 17-4 PH stainless steel

January 14, 2021
Author(s)
Jordan S. Weaver, Justin G. Whiting, Vipin Tondare, Carlos R. Beauchamp, Max A. Peltz, Jared B. Tarr, Thien Q. Phan, Mehmet Donmez
It is well known that changes in the starting powder can have a significant impact on the laser powder bed fusion process and subsequent part performance. Relationships between the powder particle size distribution and powder performance such as

Measurements of Melt Pool Geometry and Cooling Rates of Individual Laser Traces on IN625 Bare Plates

February 5, 2020
Author(s)
Brandon M. Lane, Jarred C. Heigel, Richard E. Ricker, Ivan Zhirnov, Vladimir Khromchenko, Jordan S. Weaver, Thien Q. Phan, Mark R. Stoudt, Sergey Mekhontsev, Lyle E. Levine
The complex physical nature of the laser powder bed fusion (LPBF) process warrants use of multiphysics computational simulations to predict or design optimal operating parameters or resultant part qualities such as microstructure or defect concentration

Unsupervised Learning of Dislocation Motion

October 14, 2019
Author(s)
Darren Pagan, Thien Q. Phan, Jordan Weaver, Austin Benson, Armand Beaudoin
The unsupervised learning technique, locally linear embedding (LLE), is applied to the analysis of X-ray diffraction data measured in-situ during uniaxial plastic deformation of an additively manufactured nickel-based superalloy. With the aid of a physics