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Search Publications by: Ho Yeung (Fed)

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Displaying 1 - 25 of 43

Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges

May 7, 2024
Author(s)
Jordan Weaver, David Deisenroth, Sergey Mekhontsev, Brandon Lane, Lyle E. Levine, Ho Yeung
AM Bench is a NIST-led organization that provides a continuing series of additive manufacturing (AM) benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly

MULTI-SCALE MODEL PREDICTIVE CONTROL FOR LASER POWDER BED FUSION ADDITIVE MANUFACTURING

March 20, 2024
Author(s)
Gi Suk Hong, Zhuo Yang, Yan Lu, Brandon Lane, Ho Yeung, Jaehyuk Kim
Additive manufacturing (AM) process stability is critical for ensuring part quality. Model Predictive Control (MPC) has been widely recognized as a robust technology for controlling manufacturing processes across various industries. Despite its widespread

Additive Manufacturing Data and Metadata Acquisition-General Practice

June 30, 2023
Author(s)
Yan Lu, Ho Yeung, Jason Fox, Felix Kim, Luke Mohr
Increasingly, a vast variety of additive manufacturing (AM) datasets are generated through AM development lifecycles. The amount, type, and speed of the collected data are unprecedented. The datasets are created and collected for material development

STEP-NC COMPLIANT DATA REPRESENTATIONS FOR POWDER BED FUSION IN ADDITIVE MANUFACTURING

November 11, 2022
Author(s)
Fahad Milaat, Paul Witherell, Martin Hardwick, Ho Yeung, Vincenzo Ferrero, Laetitia Monnier, Matthew Brown
Powder bed fusion (PBF) is an additive manufacturing (AM) technology that uses powerful beams to fuse powder material into layers of scanned patterns, thus producing parts with great geometric complexity. For PBF, process parameters, environmental control

STEP-NC Process Planning for Powder Bed Fusion Additive Manufacturing

October 20, 2022
Author(s)
Fahad Milaat, Paul Witherell, Martin Hardwick, Ho Yeung, Vincenzo Ferrero, Laetitia Monnier, Matthew Brown
Powder bed fusion (PBF) is an additive manufacturing (AM) technology that uses high-power beams to fuse powder material into layers of scanned patterns, thus producing parts with great geometric complexity. For PBF, the selection of appropriate process

Laser Calibration for Powder Bed Fusion Additive Manufacturing Process

July 27, 2022
Author(s)
Ho Yeung, Steven Grantham
Laser powder bed fusion systems use a high-power laser, steered by two galvanometer mirrors to scan a pattern on metal powder layers. The laser spot size, position, and synchronization between the laser power and position are critical to the build quality

HYBRID MODELING OF MELT POOL GEOMETRY IN ADDITIVE MANUFACTURING USING NEURAL NETWORKS

November 17, 2021
Author(s)
Kevontrez Jones, Zhuo Yang, Ho Yeung, Paul Witherell, Yan Lu
Laser powder-bed fusion is an additive manufacturing (AM) process that offers exciting advantages for the fabrication of metallic parts compared to traditional techniques, such as the ability to create complex geometries with less material waste. However

Characterizing the effects of laser control in laser powder bed fusion on near-surface pore formation via combined analysis of in-situ melt pool monitoring and X-ray computed tomography

October 4, 2021
Author(s)
Felix Kim, Ho Yeung, Edward Garboczi
Background: Near-surface or sub-surface pores are critical to the structural integrity of additively manufactured (AM) parts, especially in fatigue failure applications. However, their formation in laser powder bed fusion is not well-understood due to the

Numerical Evaluation of Advanced Laser Control Strategies Influence on Residual Stresses for Laser Powder Bed Fusion Systems

November 30, 2020
Author(s)
Carraturo Massimo, Brandon Lane, Ho Yeung, Stefan Kollmannsberger, Alessandro Reali, Ferdinando Auricchio
Process-dependent residual stresses are one of the main burdens to a wide spread adoption of laser powder bed fusion technology in industry. Residual stresses are directly influenced by process parameters, such as laser path, laser power, and speed. In