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Search Publications by: Yan Lu (Fed)

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Displaying 1 - 25 of 82

A Fully Registered In-Situ and Ex-Situ Dataset for Metal Powder Bed Fusion Additive Manufacturing: Data Processing, Feature Extraction, Registration, and Uncertainties

May 8, 2025
Author(s)
Zhuo Yang, Yan Lu, Ho Yeung, Brandon Lane, Nicole Van Handel
This document details the data registration process for the previously published datasets from Additive Manufacturing Metrology Testbed (AMMT) parts, "Overhang Part X4," generated at the National Institute of Standards and Technology. The two datasets —one

Metadata Modeling for Manufacturing Enterprise Integration

January 6, 2025
Author(s)
Yan Lu, Boonserm Kulvatunyou, James Jim Wilson, Michael Figura, Josh Ki, David Noller
The continuous digitalization and digital transformation are expanding enterprise information exchange scenarios that span product data, process data sets, and supply chain documents. This paper samples common manufacturing enterprise metadata exchange

Knowledge Extraction in Additive Manufacturing: a Formal Concept Analysis Approach

November 13, 2024
Author(s)
Zhuo Yang, Yan Lu, Yande Ndiaye, Mario Lezoche, Herve Panetto
In Additive Manufacturing (AM), it is still a major challenge to manage part quality, which is heavily influenced by feedstock materials, process settings, and in-process control. Deviations in these factors can lead to defects in the final product

Multi-Scale Model Predictive Control for Laser Powder Bed Fusion Additive Manufacturing

November 13, 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

Towards Reproducible Machine Learning-Based Process Monitoring and Quality Prediction Research for Additive Manufacturing

November 13, 2024
Author(s)
Yan Lu, Zhuo Yang, Jiarui Xie, Mutahar Safdar, Andrei Mircea Romascanu, Hyunwoong Ko, Yaoyao Fiona Zhao
Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). In-situ and in-process data acquired using sensors can be used to train ML models that detect process anomalies

An Overarching Quality Evaluation Framework for Additive Manufacturing Digital Twin

September 2, 2024
Author(s)
Yan Lu, Zhuo Yang, Shengyen Li, Yaoyao Fiona Zhao, Jiarui Xie, Mutahar Safdar, Hyunwoong Ko
The key differentiation of digital twins from existing models-based engineering approaches lies in the continuous synchronization between physical and virtual twins through data exchange. The success of digital twins, whether operated automatically or with

A Common Data Dictionary and Common Data Model for Additive Manufacturing

February 16, 2024
Author(s)
Alexander Kuan, Kareem Aggour, Shengyen Li, Yan Lu, Luke Mohr, Alex Kitt, Hunter Macdonald
Additive manufacturing (AM) leverages emerging technologies and well-adopted processes to produce near-net-shape products. The advancement of AM technology requires data management tools to collect, store and share information through the product

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
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