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Displaying 1 - 25 of 383

Accurate keyhole instability prediction in metal additive manufacturing through machine learning-aided numerical simulation

February 20, 2025
Author(s)
Jiahui Zhang, Runbo Jiang, Kangming Li, Pengyu Chen, Xiao Shang, Zhiying Liu, Brian Simonds, Qianglong Wei, Hongze Wang, Jason Hattrick-Simpers, Tao Sun, Anthony Rollet, Yu Zou
A primary obstacle impeding the use of metal additive manufacturing technologies in fatigue-sensitive applications is the presence of porosity, primarily caused by keyhole instability. To tackle this challenge, it is imperative to accurately forecast

Insights on grain refinement of Al-Mn-Fe-Si alloy via in situ reaction during laser direct energy deposition

February 2, 2025
Author(s)
Qingyu Pan, Fan Zhang, Deepak Vikraman Pillai, Zilong Zhang, Yufeng Zheng, Lang Yuan, Monica Kapoor, John Carsley, Xiaoyuan Lou
In the present work, we studied the grain refinement by adding in situ reactants, pure titanium (Ti) or a combination of Ti and boron (B), and investigated the governing mechanism in Al-Mn-Fe-Si 3104 alloy made by laser direct energy deposition (DED)

Correlating Titanium Powder Manufacturing Methods and Resultant Particle Morphologies to Microstructural Properties, Particle Flight and Impact Velocity, and Bonding and Deposition Characteristics in Cold Spray Additive Manufacturing

January 23, 2025
Author(s)
Pranav Anumandla, Carlos Faggi, Sinan Muftu, Edward Garboczi, Newell Moser, Rachel Cook, Nicholas Derimow, Ozan Ozdemir
Unlike high temperature thermal spray processes and metal additive manufacturing methods that require extensive heat treatment, native particle microstructural properties in the feedstock powder have been shown to dictate the final thermomechanical

Simulated inter-filament fusion in embedded 3D printing

November 15, 2024
Author(s)
Leanne Friedrich, Ross Gunther
In embedded 3D printing (EMB3D), a nozzle extrudes continuous filaments inside of a viscoelastic support bath. Compared to other extrusion processes, EMB3D enables softer structures and print paths that conform better to the shape of the part. However

Clarifying the Formation of Equiaxed Grains and Microstructural Refinement in the Additive Manufacturing of Binary Ti-Cu

November 14, 2024
Author(s)
Alec Saville, Adriana Eres-Castellanos, Andrew Kustas, Levi Van Bastian, Donald Susan, Dale Cillessen, Sven Vogel, Natalie Compton, Kester Clarke, Amy Clarke
Controlling microstructural evolution in metallic additive manufacturing (AM) is difficult, especially in producing refined as-built grains instead of coarse, directional grains. Traditional solutions involve adding inoculants to AM feedstocks, but

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

ONTOLOGY-BASED CONTEXT-AWARE DATA ANALYTICS IN ADDITIVE MANUFACTURING

November 13, 2024
Author(s)
Yeun Park, Paul Witherell, Hyunbo Cho
Recent advances in Additive Manufacturing (AM), particularly in production scenarios, have been largely driven by insights achieved through data analytics. AM has greatly benefited from the increasingly large amounts of data generated during the design to

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

In-process monitoring and non-destructive evaluation for metal additive manufacturing processes

September 17, 2024
Author(s)
Alkan Donmez, Jason Fox, Felix Kim, Brandon Lane, Maxwell Praniewicz, Vipin Tondare, Jordan Weaver, Paul Witherell
The rapid pace of maturation of metal additive manufacturing (AM) technologies, makes them an excellent candidate for the fabrication of nuclear power plant (NPP) components. However, the current levels of process variations create numerous challenges yet

Model-Based Production Operational Control for Metal Additive Manufacturing

September 15, 2024
Author(s)
Duncan Gibbons, Paul Witherell
Metal additive manufacturing processes are influenced by many variables that affect the produced material to varying degrees. This wide range of variables and the complex fusion mechanisms inherent to these processes often result in a manufacturing process

In-situ, Parallel Monitoring of Relative Temperature, Material Emission, and Laser Reflection in Powder-blown Directed Energy Deposition

September 10, 2024
Author(s)
Samantha Webster, Jihoon Jeong, Rujing Zha, Shuheng Liao, Alberto Castro, Lars Jacquemetton, Darren Beckett, Kornel Ehmann, Jian Cao
In-situ monitoring is critical for developing new control methods, advanced materials, and toolpath planning strategies in laser beam Directed Energy Deposition (DED-LB). Coaxial melt pool monitoring has typically been performed with cameras (e.g
Displaying 1 - 25 of 383