Publications

Rodgers, Theron R.; Moser, Daniel M.; Abdeljawad, Fadi F.; Martinez, Mario J.; Ford, Kurtis R.; Trembacki, Bradley T.; Johnson, Kyle J.; Mitchell, John A.; Madison, Jonathan D.

Abstract not provided.

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.

Swiler, Laura P.; van Bloemen Waanders, Bart G.; Jared, Bradley H.; Koepke, Joshua R.; Whetten, Shaun R.; Madison, Jonathan D.; Ivanoff, Thomas I.; Jackson, Olivia D.; 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.

Johnson, Kyle J.; Swiler, Laura P.; Rodgers, Theron R.; Jared, Bradley H.; Subia, Samuel R.; Ford, Kurtis R.; Stender, Michael S.; Bishop, Joseph E.

Abstract not provided.

Roach, R.A.; Bishop, Joseph E.; Jared, Bradley H.; Keicher, David M.; Cook, Adam W.; Whetten, Shaun R.; Forrest, Eric C.; Stanford, Joshua S.; Boyce, Brad B.; Johnson, Kyle J.; Rodgers, Theron R.; Ford, Kurtis R.; Martinez, Mario J.; Moser, Daniel M.; van Bloemen Waanders, Bart G.; Chandross, M.; Abdeljawad, Fadi F.; Allen, Kyle M.; Stender, Michael S.; Beghini, Lauren L.; Swiler, Laura P.; Lester, Brian T.; Argibay, Nicolas A.; Brown-Shaklee, Harlan J.; Kustas, Andrew K.; Sugar, Joshua D.; Kammler, Daniel K.; Wilson, Mark A.

Abstract not provided.

Silling, Stewart A.; Abdeljawad, Fadi A.; Ford, Kurtis R.

Sintering is a component fabrication process in which powder is compacted by pressing or some other means and then held at elevated temperature for a period of hours. The powder grains bond with each other, leading to the formation of a solid component with much lower porosity, and therefore higher density and higher strength, than the original powder compact. In this project, we investigated a new way of computationally modeling sintering at the length scale of grains. The model uses a high-fidelity, three-dimensional representation with a few hundred nodes per grain. The numerical model solves the peridynamic equations, in which nonlocal forces allow representation of the attraction, adhesion, and mass diffusion between grains. The deformation of the grains is represented through a viscoelastic material model. The project successfully demonstrated the use of this method to reproduce experimentally observed features of material behavior in sintering, including densification, the evolution of microstructure, and the occurrence of random defects in the sintered solid.