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Displaying 1 - 25 of 333

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

An Overarching Quality Evaluation Framework for Additive Manufacturing Digital Twin

April 15, 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

Evaluating Models That Predict Epoxy Conversion Using Rheological Properties

April 15, 2024
Author(s)
Stian Romberg, Paul Roberts, Chad R. Snyder, Anthony Kotula
Simultaneous rheology and conversion measurements of neat and composite epoxy resins reveal that conventional models neither accurately nor fully describe the relationship between rheology and conversion. We find that models predicting thermoset conversion

KNOWLEDGE EXTRACTION IN ADDITIVE MANUFACTURING A FORMAL CONCEPT ANALYSIS APPROACH

March 20, 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

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

TOWARDS REPRODUCIBLE MACHINE LEARNING-BASED PROCESS MONITORING AND QUALITY PREDICTION RESEARCH FOR ADDITIVE MANUFACTURING

March 20, 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

Results of an Interlaboratory Study on the Working Curve in Vat Photopolymerization

March 18, 2024
Author(s)
Thomas Kolibaba, Jason Killgore, Benjamin Caplins, Callie Higgins, Uwe Arp, C Cameron Miller, Yuqin Zong, Dianne L. Poster
The working curve informs resin properties and print parameters for stereolithography, digital light processing, and other photopolymer additive manufacturing (PAM) technologies. First demonstrated in 1992, the working curve measurement of cure depth vs

The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

February 28, 2024
Author(s)
Connor V. Headley, Roberto J. Herrera del Valle, Ji Ma, Prasanna Balachandran, Vijayabarathi Ponnambalam, Saniya LeBlanc, Dylan Kirsch, Joshua B. Martin
Through the integration of machine learning (ML) techniques alongside additive manufacturing (AM) experimentation, we demonstrate an iterative process to rapidly predict laser-material interactions and melt pool geometries throughout the build parameter

Phase Composition and Phase Transformation of Additively Manufactured Nickel Alloy 718 AM Bench Artifacts

February 5, 2024
Author(s)
Fan Zhang, Aaron Johnston-Peck, Lyle E. Levine, Michael Katz, Kil-Won Moon, Maureen E. Williams, Sandra W. Young, Andrew J. Allen, Olaf Borkiewicz, Jan Ilavsky
Additive Manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation
Displaying 1 - 25 of 333