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Solid Freeform Fabrication 2019: Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2019

Taylor, Samantha T.; Jared, Bradley H.; Koepke, Joshua R.; Forrest, Eric C.; Beaman, Joseph

Additive manufacturing (AM) is known for its large variance in mechanical properties. This is not only true for properties like strength, but also surface roughness. Build settings, which affect surface roughness, are often chosen to optimize strength or ductility. As part requirements change, build settings change, thereby changing resultant surface roughness. When describing surfaces, arithmetic roughness (Ra) is the most common parameter. However, it may not provide an adequate representation of surface topography for AM parts. Traditional surface roughness parameters for defining surface topography were well-established before the advent of AM, and a need has arisen to investigate applicability of these parameters to the unusual surfaces created through various AM technologies. This study demonstrates that Ra is not a suitable parameter in correlating surface topography to AM build parameters. Other existing parameters and combination of parameters will be investigated for their suitability in describing the AM process.

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TYPE Conference Poster YEAR 2019

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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

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