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Search Publications by: Jason Fox (Fed)

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Displaying 26 - 50 of 55

Toward specification of complex additive manufactured metal surfaces for optimum heat transfer

September 23, 2021
Author(s)
Kuldeep Mandloi, Christopher Evans, Jason Fox, Harish Cherukuri, Jimmie Miller, Angela Allen, David Deisenroth, Alkan Donmez
Metal additive manufacturing (AM) offers the possibility of incorporating cooling channels into components in high-temperature applications. Additionally, it has the prospect of optimizing cooling channel geometry unconstrained by geometric limitations of

Data registration for multi-method qualification of additive manufactured components

August 25, 2021
Author(s)
Maxwell R. Praniewicz, Gaurav Ameta, Jason Fox, Christopher Saldana
This work explores impact of refined surface registrations of voxel and point cloud data sets on accuracy of multi-method qualification of additively manufactured (AM) lattices. Voxel and point cloud sets of an AM lattice were aligned using derived

Exploring Registration of Optical, CMM and XCT for Verification of Supplemental Surfaces to Define AM Lattices: Application to Cylindrical and Spherical Surfaces

August 25, 2021
Author(s)
Maxwell Praniewicz, Jason Fox, Gaurav Ameta, Felix Kim, Paul Witherell, Christopher Saldana
The use of lattice structures produced using additive manufacturing (AM) is of great interest to the aerospace and medical industries because of their potential for strength/weight optimization. However, their use is often limited due to challenges in

Merging experiments and computer simulations in X-ray Computed Tomography probability of detection analysis of additive manufacturing flaws

January 27, 2021
Author(s)
Felix Kim, Adam L. Pintar, Jason Fox, Jared B. Tarr, Alkan Donmez, Anne-Francoise Obaton
X-ray Computed Tomography (XCT) is a growing industrial non-destructive testing (NDT) technique for advanced manufacturing industries such as additive manufacturing (AM). Probability of detection (POD) is a critical aspect for qualifying NDT techniques

Data-driven characterization of computational models for powder-bed-fusion additive manufacturing

July 31, 2020
Author(s)
Yan Lu, Zhuo Yang, Paul W. Witherell, Wentao Yan, Kevontrez Jones, Gregory Wagner, Wing-Kam Liu, Jason C. Fox
Computational modeling for additive manufacturing has proven to be a powerful tool to understand the physical mechanisms, predict fabrication quality, and guide design and optimization. Varieties of models have been developed with different assumptions and

Outcomes and Conclusions from the 2018 AM-Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

February 13, 2020
Author(s)
Lyle E. Levine, Brandon M. Lane, Jarred C. Heigel, Kalman D. Migler, Mark R. Stoudt, Thien Q. Phan, Richard E. Ricker, Maria Strantza, Michael R. Hill, Fan Zhang, Jonathan E. Seppala, Edward J. Garboczi, Erich D. Bain, Daniel Cole, Andrew J. Allen, Jason C. Fox, Carelyn E. Campbell
The Additive Manufacturing Benchmark Test Series (AM-Bench) was established to provide rigorous measurement test data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM-Bench includes

PROBABILITY OF DETECTION OF X-RAY COMPUTED TOMOGRAPHY OF ADDITIVE MANUFACTURING DEFECTS

July 19, 2019
Author(s)
Felix H. Kim, Adam L. Pintar, Jason C. Fox, Jared B. Tarr, M A. Donmez, Anne-Fran?oise Obaton
A methodology to determine probability of detection (POD) of X-ray Computed Tomography (XCT) was developed using Additive Manufacturing defects. A signal response POD analysis (a^ vs a) was used, where both signal response (a^) and true defect size (a)

Complementary Use of Optical Metrology and X-Ray Computed Tomography for Surface Finish and Defect Detection in Laser Powder Bed Fusion Additive Manufacturing

July 25, 2018
Author(s)
Jason C. Fox, Felix H. Kim, Zachary C. Reese, Christopher Evans
The development of additive manufacturing (AM) has allowed for production of high-value and complex parts that reduce time-to-market and cost to manufacture. A key benefit to the AM process is the capability to create lattice structures and highly complex

Implementation of Advanced Laser Control Strategies for Powder Bed Fusion Systems

June 21, 2018
Author(s)
Ho Yeung, Brandon Lane, Alkan Donmez, Jason Fox, Jorge Neira
Laser path, scan speed, and laser power are critical machine parameters determining the quality of the output of laser-based powder bed fusion (LPBF) processes. A jerk-limited control strategy is implemented for laser path planning on a LPBF additive

Continuous Laser Scan Strategy for Faster Build Speeds in Laser Powder Bed Fusion System

August 9, 2017
Author(s)
Ho Yeung, Brandon Lane, Jason Fox, Felix Kim, Jarred C. Heigel, Jorge Neira
Research has shown significant influence of laser scan strategy on various part qualities in the laser powder bed fusion additive manufacturing process. The National Institute of Standards and Technology developed the Additive Manufacturing Metrology
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