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Search Publications by: Zhuo Yang (IntlAssoc)

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Displaying 1 - 22 of 22

Investigating Statistical Correlation Between Multiple In-Situ Monitoring Datasets for Powder Bed Fusion Additive Manufacturing

August 24, 2022
Author(s)
Zhuo Yang, Yan Lu, Milica Perisic, Yande Ndiaye, Adnan Gujjar, Fan-Tien Cheng, Haw-Ching Yang
In-situ measurements provide vast information for additive manufacturing process understanding and real-time control. Data from various monitoring techniques observes different characteristics of a build process. Fusing multi-modal in-situ monitoring data

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

IN-PROCESS DATA FUSION FOR PROCESS MONITORING AND CONTROL OF METAL ADDITIVE MANUFACTURING

November 17, 2021
Author(s)
Zhuo Yang, Yan Lu, Simin Li, Jennifer Li, Yande Ndiaye, Hui Yang, Sundar Krishnamurty
To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the

Cognitive Automation and its Impact on Additive Manufacturing

October 15, 2020
Author(s)
Albert T. Jones, Zhuo Yang, Yan Lu
The English word manufacturing firstly appeared in 1683 and it was derived from Latin manu factus, meaning making by hand. For more than thousands of years now, and four Industrial Revolutions, the physical, and mostly mechanical processes, associated with

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

FROM SCAN STRATEGY TO MELT POOL PREDICTION: A NEIGHBORING-EFFECT MODELING METHOD

April 23, 2020
Author(s)
Zhuo Yang, Yan Lu, Ho Yeung, Sundar Krishnamurty
The quality of AM built parts is highly correlated to the melt pool characteristics. Hence melt pool monitoring and control can potentially improve AM part quality. This paper presents a neighboring-effect modeling method (NBEM) that uses scan strategy to