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Displaying 301 - 325 of 393

Outcomes and Conclusions from the 2018 AM-Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

February 13, 2020
Author(s)
Lyle E. Levine, Brandon M. Lane, Jarred C. Heigel, Kalman D. Migler, Mark R. Stoudt, Thien Q. Phan, Richard E. Ricker, Maria Strantza, Michael R. Hill, Fan Zhang, Jonathan E. Seppala, Edward J. Garboczi, Erich D. Bain, Daniel Cole, Andrew J. Allen, Jason C. Fox, Carelyn E. Campbell
The Additive Manufacturing Benchmark Test Series (AM-Bench) was established to provide rigorous measurement test data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM-Bench includes

Compressive deformation analysis of large area pellet-fed material extrusion 3D printed parts in relation to in situ thermal imaging*

February 8, 2020
Author(s)
Eduardo M. Trejo, Xavier Jimenez, Kazi M. Billah, Jonathan Seppala, Ryan Wicker, David Esplain
In large area pellet extrusion additive manufacturing, the temperature of the substrate just before the deposition of a new subsequent layer affects the overall structure of the part. Warping and cracking occur if the substrate temperature is below a

Measurements of Melt Pool Geometry and Cooling Rates of Individual Laser Traces on IN625 Bare Plates

February 5, 2020
Author(s)
Brandon M. Lane, Jarred C. Heigel, Richard E. Ricker, Ivan Zhirnov, Vladimir Khromchenko, Jordan S. Weaver, Thien Q. Phan, Mark R. Stoudt, Sergey Mekhontsev, Lyle E. Levine
The complex physical nature of the laser powder bed fusion (LPBF) process warrants use of multiphysics computational simulations to predict or design optimal operating parameters or resultant part qualities such as microstructure or defect concentration

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

Unsupervised Learning of Dislocation Motion

October 14, 2019
Author(s)
Darren Pagan, Thien Q. Phan, Jordan Weaver, Austin Benson, Armand Beaudoin
The unsupervised learning technique, locally linear embedding (LLE), is applied to the analysis of X-ray diffraction data measured in-situ during uniaxial plastic deformation of an additively manufactured nickel-based superalloy. With the aid of a physics

Topographic Measurement of Individual Laser Tracks in Alloy 625 Bare Plates

October 10, 2019
Author(s)
Richard E. Ricker, Jarred C. Heigel, Brandon M. Lane, Ivan Zhirnov, Lyle E. Levine
Additive manufacturing (AM) combines all of the complexities of materials processing and manufacturing into a single process. The digital revolution made this combination possible, but the commercial viability of these technologies for critical parts may

Hot isostatic pressing (HIP) to achieve isotropic microstructure and retain as-built strength in additive manufacturing titanium alloy (Ti-6Al-4V)

September 23, 2019
Author(s)
Jake T. Benzing, Nikolas W. Hrabe, Timothy P. Quinn, Ryan M. White, Ross A. Rentz, Magnus Ahlfors
Hot isostatic pressing (HIP) treatments are used to seal internal porosity because defects exist in as-built Ti-6Al-4V parts produced by electron beam melting powder bed fusion. Standard HIP treatment of Ti-6Al-4V parts reduces internal porosity but

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
Displaying 301 - 325 of 393
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