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Evolution of 316L stainless steel feedstock due to laser powder bed fusion process

Additive Manufacturing

Heiden, Michael J.; Deibler, Lisa A.; Rodelas, Jeffrey R.; Koepke, Joshua R.; Tung, Daniel J.; Saiz, David J.; Jared, Bradley H.

Some of the primary barriers to widespread adoption of metal additive manufacturing (AM) are persistent defect formation in built components, high material costs, and lack of consistency in powder feedstock. To generate more reliable, complex-shaped metal parts, it is crucial to understand how feedstock properties change with reuse and how that affects build mechanical performance. Powder particles interacting with the energy source, yet not consolidated into an AM part can undergo a range of dynamic thermal interactions, resulting in variable particle behavior if reused. In this work, we present a systematic study of 316L powder properties from the virgin state through thirty powder reuses in the laser powder bed fusion process. Thirteen powder characteristics and the resulting AM build mechanical properties were investigated for both powder states. Results show greater variability in part ductility for the virgin state. The feedstock exhibited minor changes to size distribution, bulk composition, and hardness with reuse, but significant changes to particle morphology, microstructure, magnetic properties, surface composition, and oxide thickness. Additionally, sieved powder, along with resulting fume/condensate and recoil ejecta (spatter) properties were characterized. Formation mechanisms are proposed. It was discovered that spatter leads to formation of single crystal ferrite through large degrees of supercooling and massive solidification. Ferrite content and consequently magnetic susceptibility of the powder also increases with reuse, suggesting potential for magnetic separation as a refining technique for altered feedstock.

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TYPE Journal Article YEAR 2019
DOIOSTIScopus

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
DOIOSTI

Data Analysis for the Born Qualified Grand LDRD Project

Swiler, Laura P.; van Bloemen Waanders, Bart G.; Jared, Bradley H.; Koepke, Joshua R.; Whetten, Shaun R.; Madison, Jonathan D.; Ivanoff, Thomas I.; Laros, James H.; Cook, Adam W.; Brown-Shaklee, Harlan J.; Kammler, Daniel K.; Johnson, Kyle J.; Ford, Kurtis R.; Bishop, Joseph E.; Roach, R.A.

This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processe s for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.

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TYPE SAND Report YEAR 2018
DOIOSTI
Results 76–100 of 220
Results 76–100 of 220