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Heterogeneities dominate mechanical performance of additively manufactured metal lattice struts

Additive Manufacturing

Dressler, Amber D.; Jost, Elliott W.; Miers, John C.; Moore, David G.; Seepersad, Carolyn C.; Boyce, Brad B.

Architected structural metamaterials, also known as lattice, truss, or acoustic materials, provide opportunities to produce tailored effective properties that are not achievable in bulk monolithic materials. These topologies are typically designed under the assumption of uniform, isotropic base material properties taken from reference databases and without consideration for sub-optimal as-printed properties or off-nominal dimensional heterogeneities. However, manufacturing imperfections such as surface roughness are present throughout the lattices and their constituent struts create significant variability in mechanical properties and part performance. This study utilized a customized tensile bar with a gauge section consisting of five parallel struts loaded in a stretch (tensile) orientation to examine the impact of manufacturing heterogeneities on quasi-static deformation of the struts, with a focus on ultimate tensile strength and ductility. The customized tensile specimen was designed to prevent damage during handling, despite the sub-millimeter thickness of each strut, and to enable efficient, high-throughput mechanical testing. The strut tensile specimens and reference monolithic tensile bars were manufactured using a direct metal laser sintering (also known as laser powder bed fusion or selective laser melting) process in a precipitation hardened stainless steel alloy, 17-4PH, with minimum feature sizes ranging from 0.5-0.82 mm, comparable to minimum allowable dimensions for the process. Over 70 tensile stress-strain tests were performed revealing that the effective mechanical properties of the struts were highly stochastic, considerably inferior to the properties of larger as-printed reference tensile bars, and well below the minimum allowable values for the alloy. Pre- and post-test non-destructive analyses revealed that the primary source of the reduced properties and increased variability was attributable to heterogeneous surface topography with stress-concentrating contours and commensurate reduction in effective load-bearing area.

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TYPE Journal Article YEAR 2019
ScopusOSTIDOI

Born Qualified Grand Challenge LDRD Final Report

Roach, R.A.; Argibay, Nicolas A.; Allen, Kyle M.; Balch, Dorian K.; Beghini, Lauren L.; Bishop, Joseph E.; Boyce, Brad B.; Brown, Judith A.; Burchard, Ross L.; Chandross, M.; Cook, Adam W.; DiAntonio, Christopher D.; Dressler, Amber D.; Forrest, Eric C.; Ford, Kurtis R.; Ivanoff, Thomas I.; Jared, Bradley H.; Johnson, Kyle J.; Kammler, Daniel K.; Koepke, Joshua R.; Kustas, Andrew K.; Lavin, Judith M.; Leathe, Nicholas L.; Lester, Brian T.; Madison, Jonathan D.; Mani, Seethambal S.; Martinez, Mario J.; Moser, Daniel M.; Rodgers, Theron R.; Seidl, Daniel T.; Brown-Shaklee, Harlan J.; Stanford, Joshua S.; Stender, Michael S.; Sugar, Joshua D.; Swiler, Laura P.; Taylor, Samantha T.; Trembacki, Bradley T.

This SAND report fulfills the final report requirement for the Born Qualified Grand Challenge LDRD. Born Qualified was funded from FY16-FY18 with a total budget of ~$13M over the 3 years of funding. Overall 70+ staff, Post Docs, and students supported this project over its lifetime. The driver for Born Qualified was using Additive Manufacturing (AM) to change the qualification paradigm for low volume, high value, high consequence, complex parts that are common in high-risk industries such as ND, defense, energy, aerospace, and medical. AM offers the opportunity to transform design, manufacturing, and qualification with its unique capabilities. AM is a disruptive technology, allowing the capability to simultaneously create part and material while tightly controlling and monitoring the manufacturing process at the voxel level, with the inherent flexibility and agility in printing layer-by-layer. AM enables the possibility of measuring critical material and part parameters during manufacturing, thus changing the way we collect data, assess performance, and accept or qualify parts. It provides an opportunity to shift from the current iterative design-build-test qualification paradigm using traditional manufacturing processes to design-by-predictivity where requirements are addressed concurrently and rapidly. The new qualification paradigm driven by AM provides the opportunity to predict performance probabilistically, to optimally control the manufacturing process, and to implement accelerated cycles of learning. Exploiting these capabilities to realize a new uncertainty quantification-driven qualification that is rapid, flexible, and practical is the focus of this effort.

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TYPE SAND Report YEAR 2018
OSTIDOI
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11 Results