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Search Publications by: Yan Lu (Fed)

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Displaying 51 - 75 of 80

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

DATA REGISTRATION FOR IN-SITU MONITORING OF LASER POWDER BED FUSION PROCESSES

November 11, 2019
Author(s)
Shaw C. Feng, Yan Lu, Albert W. Jones
Increasingly, a wide range of in-situ sensors are being instrumented on additive manufacturing (AM) machines. Researchers and manufacturers use these sensors to collect a variety of data to monitor process performance and part quality. The amount and speed

Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing

September 19, 2019
Author(s)
Hyunwoong Ko, Yan Lu, Paul W. Witherell, Ndeye Y. Ndiaye
Additive manufacturing (AM) assisted by a digital twin is expected to revolutionize the realization of high-value and high-complexity functional parts on a global scale. With machine learning (ML) introduced in the AM digital twin, AM data are transformed

Foundations of information governance for smart manufacturing

June 11, 2019
Author(s)
KC Morris, Yan Lu, Simon P. Frechette
The manufacturing systems of the future will be even more heavily dependent on the data than they are today. More and more data and information are being collected and communicated throughout product development lifecycles and across manufacturing value

2018 NIST/OAGi Workshop: Enabling Composable Service-Oriented Manufacturing Systems

April 22, 2019
Author(s)
Nenad Ivezic, Boonserm Kulvatunyou, Michael P. Brundage, Yan Lu, Evan K. Wallace, Albert W. Jones
This report summarizes the results from the 2018 NIST/OAGi Workshop: Enabling Composable Service-Oriented Manufacturing Systems, which was held at the National Institute of Standards and Technology campus in Gaithersburg, MD, on April 23-24, 2018. This was

SELF-IMPROVING ADDITIVE MANUFACTURING KNOWLEDGE MANAGEMENT

August 26, 2018
Author(s)
Yan Lu, Zhuo Yang, Douglas Eddy, Sundar Krishnamurty
The current AM development environment is far from being mature. Both software applications and workflow management tools are very limited due to the lack of knowledge to support engineering decision makings. AM knowledge includes design rules, operation

A SUPER-METAMODELLING FRAMEWORK TO OPTIMIZE SYSTEM PREDICTABILITY

August 25, 2018
Author(s)
Yan Lu, Douglas Eddy, Sundar Krishnamurty, Ian Grosse
Statistical metamodels can robustly predict manufacturing process and engineering systems design results. Various techniques, such as Kriging, polynomial regression, artificial neural network and others, are each best suited for different scenarios that

Condition-based Real-time Production Control for Smart Manufacturing Systems

August 19, 2018
Author(s)
Yan Lu, Feifan Wang, Feng Ju
In this paper, we present condition-based real-time production control for smart manufacturing which is aimed at improving system performance by automatically assessing a production system's condition and dynamically configuring the processing routes for

A DOMAIN DRIVEN APPROACH TO METAMODELING IN ADDITIVE MANUFACTURING

September 6, 2017
Author(s)
Peter O. Denno, Yan Lu, Paul Witherell, Sundar Krishnamurty, Ian Grosse, Douglas Eddy
Recent studies have shown advantages to utilizing metamodeling techniques to mimic, analyze, and optimize system input-output relationships in Additive Manufacturing (AM). This paper addresses a key challenge in applying such metamodeling methods, namely

A Collaborative Data Management System for Additive Manufacturing

August 9, 2017
Author(s)
Yan Lu, Paul W. Witherell, M A. Donmez
As additive manufacturing (AM) continues to mature as a production technology, the limiting factors that have hindered its adoption in the past still exist, for example, process repeatability and material availability issues. Overcoming many of these

INVESTIGATING GREY-BOX MODELING FOR PREDICTIVE ANALYTICS IN SMART MANUFACTURING

August 8, 2017
Author(s)
Peter O. Denno, Yan Lu, Paul Witherell, Sundar Krishnamurty, Ian Grosse, Douglas Eddy
This paper develops a grey-box modeling approach that combines manufacturing knowledge-based (white-box) models with statistical (black-box) metamodels to improve model reusability and predictability. A white-box model can utilize different types of

Building Connections through Standards Landscaping

June 7, 2017
Author(s)
Paul W. Witherell, Yan Lu
Standards are an essential part of how we do business, from communication, to establishing best practices, to achieving quality control. As commerce and technology continues to diversify, standards, and standard organizations, have become increasingly

NIST/OAGi Workshop: Drilling down on Smart Manufacturing – Enabling Composable Apps

April 19, 2017
Author(s)
Nenad Ivezic, Boonserm Kulvatunyou, Hyunbo Cho, Yan Lu, Jim Davis, Thorsten Wuest, Farhad Ameri, William Z. Bernstein
This report summarizes the results from the OAGi/NIST workshop Drilling down on Smart Manufacturing -- Enabling Composable Apps, which was held at the National Institute of Standards and Technology campus in Gaithersburg, MD, on April 18-19, 2016. The

Towards a Digital Thread and Data Package for Metals Additive Manufacturing

March 6, 2017
Author(s)
Paul Witherell, Yan Lu, Shaw C. Feng, Duckbong Kim
Additive manufacturing (AM) has been envisioned by many as the next industrial revolution. Potential benefits of AM include the production of low-volume, customized, complicated parts/products, supply chain efficiencies, shortened time-to-market, and

The Paradigm Shift in Smart Manufacturing System Ar-chitecture

September 4, 2016
Author(s)
Yan Lu, Frank H. Riddick, Nenad Ivezic
In response to demanding market conditions, Smart Manufacturing (SM) seeks to integrate advanced manufacturing methods, operational technologies (OT), and information and communication technologies (ICT) to drive the creation of manufacturing systems with

DIGITAL SOLUTIONS FOR INTEGRATED AND COLLABORATIVE ADDITIVE MANUFACTURING

August 23, 2016
Author(s)
Yan Lu, Paul Witherell, Felipe F. Lopez, Ibrahim Assouroko
Software tools, knowledge of materials and process models, and data provide three pillars on which Additive Manufacturing (AM) lifecycles and value chains can be supported. These pillars leverage efforts dedicated to the development of AM databases, high

Additive Manufacturing: A Trans-disciplinary Experience

July 20, 2016
Author(s)
Paul W. Witherell, Albert W. Jones, Yan Lu
Traditional manufacturing has long permitted disciplines to operate in isolation: with designers, material suppliers, and manufactures often able to function independently towards the singular goal of creating a product. As products have become

Implementing the ISO 15746 Standard for Chemical Process Optimization

July 3, 2016
Author(s)
Guodong Shao, Peter O. Denno, Albert T. Jones, Yan Lu
Integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, enables more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and