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Manufacturing

Additive manufacturing

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Displaying 301 - 325 of 388

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

Influence of optical system operation on stability of single tracks in selective laser melting

August 20, 2019
Author(s)
Ivan Zhirnov, Igor Yadroitsev, Brandon Lane, Sergey Mekhontsev, Steven Grantham, Ina Yadroitsava
Additive manufacturing (AM) technologies are increasingly being studied and introduced into the modern industry, but for wide applications there exists some "lack of confidence" about the quality of the parts produced by AM. This distrust has an objective

A Review Of Machine Learning Applications In Additive Manufacturing

August 17, 2019
Author(s)
Saadia A. Razvi, Shaw C. Feng, Anantha Narayanan Narayanan, Yung-Tsun Lee, Paul Witherell
Variability in product quality continues to pose a major barrier to the widespread application of additive manufacturing (AM) processes in production environment. Towards addressing this barrier, the monitoring of AM processes and the measuring of AM

Elastic residual strain and stress measurements and corresponding part deflections of 3D AM builds of IN625 AM-Bench artifacts using neutron diffraction, synchrotron X-ray diffraction, and contour method

July 29, 2019
Author(s)
Thien Q. Phan, Maria Strantza, Michael R. Hill, Thomas H. Gnaupel-Herold, Jarred C. Heigel, Christopher D'Elia, DeWald Adrian, Bjorn Clausen, Darren C. Pagan, J. Y. Peter Ko, Donald W. Brown, Lyle E. Levine
One of the primary barriers for acceptance of additive manufacturing (AM) has been the uncertainty in the performance of AM parts due to residual stresses/strains. The rapid heating and cooling rates, along with the thermal history of the laser melting
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