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High-throughput stochastic tensile performance of additively manufactured stainless steel

Journal of Materials Processing Technology

Boyce, Brad B.; Salzbrenner, Bradley S.; Rodelas, Jeffrey R.; Madison, Jonathan D.; Jared, Bradley H.; Swiler, Laura P.; Shen, Yu L.

An adage within the Additive Manufacturing (AM) community is that “complexity is free”. Complicated geometric features that normally drive manufacturing cost and limit design options are not typically problematic in AM. While geometric complexity is usually viewed from the perspective of part design, this advantage of AM also opens up new options in rapid, efficient material property evaluation and qualification. In the current work, an array of 100 miniature tensile bars are produced and tested for a comparable cost and in comparable time to a few conventional tensile bars. With this technique, it is possible to evaluate the stochastic nature of mechanical behavior. The current study focuses on stochastic yield strength, ultimate strength, and ductility as measured by strain at failure (elongation). However, this method can be used to capture the statistical nature of many mechanical properties including the full stress-strain constitutive response, elastic modulus, work hardening, and fracture toughness. Moreover, the technique could extend to strain-rate and temperature dependent behavior. As a proof of concept, the technique is demonstrated on a precipitation hardened stainless steel alloy, commonly known as 17-4PH, produced by two commercial AM vendors using a laser powder bed fusion process, also commonly known as selective laser melting. Using two different commercial powder bed platforms, the vendors produced material that exhibited slightly lower strength and markedly lower ductility compared to wrought sheet. Moreover, the properties were much less repeatable in the AM materials as analyzed in the context of a Weibull distribution, and the properties did not consistently meet minimum allowable requirements for the alloy as established by AMS. The diminished, stochastic properties were examined in the context of major contributing factors such as surface roughness and internal lack-of-fusion porosity. This high-throughput capability is expected to be useful for follow-on extensive parametric studies of factors that affect the statistical reliability of AM components.

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TYPE Journal Article YEAR 2017
DOIOSTIScopus

High Performance Computing - Power Application Programming Interface Specification Version 2.0

Laros, James H.; Grant, Ryan E.; Levenhagen, Michael J.; Olivier, Stephen L.; Laros, James H.; Ward, Harry L.; Younge, Andrew J.

Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.

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TYPE SAND Report YEAR 2017
DOIOSTI

Efficient Gate Set Tomography on a Multi-Qubit Superconducting Processor

Nielsen, Erik N.; Rudinger, Kenneth M.; Blume-Kohout, Robin J.; Bestwick, Andrew; Bloom, Benjamin; Block, Maxwell; Caldwell, Shane M.; Curtis, Michael J.; Hudson, Alex; Orgiazzi, Jean-Luc; Papageorge, Alexander; Polloreno, Anthony; Reagor, Matt; Rubin, Nicholas; Scheer, Michael; Selvanayagam, Michael; Sete, Eyob; Sinclair, Rodney; Smith, Robert; Vahidpour, Mehrnoosh; Villiers, Marius; Zeng, William J.; Rigetti, Chad

Abstract not provided.

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TYPE Conference Poster YEAR 2017
OSTIOSTI
Results 4151–4200 of 9,998
Results 4151–4200 of 9,998