U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: Yan Lu (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 1 - 25 of 68

Toward a Standard Data Architecture for Additive Manufacturing

January 16, 2024
Author(s)
Shengyen Li, Shaw C. Feng, Alexander Kuan, Yan Lu
To advance the additive manufacturing (AM) technologies, R&D projects may evaluate new facilities and different processes that need a scalable data architecture to accommodate the progressing knowledge. This work introduces a data pedigree to enable the

Enabling FAIR Data in Additive Manufacturing to Accelerate Industrialization

July 24, 2023
Author(s)
Shengyen Li, Yan Lu, Kareem Aggour, Peter Coutts, Brennan Harris, Alex Kitt, Afina Lupulescu, Luke Mohr, Mike Vasquez
Additive manufacturing (AM) is an important enabler of Industry 4.0 but there are several hurdles that need to be overcome to fully realize the potential of AM. These challenges include the need for a data infrastructure to enable the scaling of the

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

Additive Manufacturing Data Integration and Recommended Practice

June 30, 2023
Author(s)
Yan Lu, Milica Perisic, Albert T. Jones
Additive manufacturing (AM) creates parts layer by layer directly from three-dimensional computer-aided design data. Building in layers allows the fabrication of complex geometric shapes as well as functionally graded materials. Despite the part-quality

In-Process Data Integration for Laser Powder Bed Fusion Additive Manufacturing

November 11, 2022
Author(s)
Milica Perisic, Yan Lu, Albert T. Jones
Additive manufacturing (AM) is a powerful technology that can create complex metallic parts and has the potential to improve the economic bottom line for various industries. However, due to process instabilities, and the resulting material defects that

Research and Application of Machine Learning for Additive Manufacturing

October 1, 2022
Author(s)
Paul Witherell, Yan Lu, Ying Liu, David W. Rosen, Timothy Simpson, Charlie Wang
Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible production, on-demand and decentralized manufacturing. However, a number of challenges stem from not

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

Spatiotemporal Monitoring of Melt-Pool Variations in Metal-Based Additive Manufacturing

July 1, 2022
Author(s)
Siqing Zhang, Yan Lu, Paul Witherell, Timothy Simpson, Soundar Kumara, Hui Yang
Additive manufacturing provides a higher level of flexibility to build customized products with complex geometries. However, AM is currently limited in its ability to ensure quality assurance and process repeatability. Advanced imaging provides unique

A Data Integration Framework for Additive Manufacturing Big Data Management

December 7, 2021
Author(s)
Milica Perisic, Dimitrije Milenkovic, Yan Lu, Albert T. Jones, Nenad Ivezic, Boonserm Kulvatunyou
Large amounts of data are generated throughout the entire, AM, part-development lifecycle. Data are generated by various functions within process monitoring, material characterization, equipment status, and part qualification. Hence, data integration and

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