Publications

Robbins, Joshua R.; Aguilo Valentin, Miguel A.; Alberdi, Ryan A.; Clark, Brett W.; Jared, Bradley H.; Jensen, Scott C.; Johnson, Kyle J.; Viertel, Ryan V.

This report provides detailed documentation of the algorithms that where developed and implemented in the Plato software over the course of the Optimization-based Design for Manufacturing LDRD project.

Johnson, Kyle J.; Maestas, Demitri M.; Noell, Philip N.; Lim, Hojun L.; Emery, John M.; Smith, Matthew C.; Martinez, Carianne M.; Davis, Warren L.

Abstract not provided.

Aguilo Valentin, Miguel A.; Clark, Brett W.; Johnson, Kyle J.; Robbins, Joshua R.; Viertel, Ryan V.

Abstract not provided.

Kramer, Sharlotte L.; Bolintineanu, Dan S.; Long, Kevin N.; Hamel, Craig H.; Frankel, Ari L.; Jones, Reese E.; Swiler, Laura P.; Johnson, Kyle J.

Abstract not provided.

Solid Freeform Fabrication 2018: Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2018

Roach, R.A.; Bishop, Joseph E.; Johnson, Kyle J.; Rodgers, Theron R.; Boyce, B.L.; Swiler, L.; van Bloemen Waanders, Bart G.; Chandross, M.; Kammler, Daniel K.; Balch, Dorian K.; Jared, B.; Martinez, Mario J.; Leathe, Nicholas L.; Ford, K.

Additive Manufacturing (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 paper.

Aguilo Valentin, Miguel A.; Clark, Brett W.; Jared, Bradley H.; Johnson, Kyle J.; Robbins, Joshua R.; Russ, Jonathan B.

Abstract not provided.

Roach, R.A.; Jared, Bradley H.; Boyce, Brad B.; Lechman, Jeremy B.; Martinez, Mario A.; Forrest, Eric C.; Taylor, Samantha T.; Dagel, Amber L.; Madison, Jonathan D.; Ivanoff, Thomas I.; Koepke, Joshua R.; Swiler, Laura P.; van Bloemen Waanders, Bart G.; Clark, Brett W.; Rodgers, Theron R.; Johnson, Kyle J.

Abstract not provided.

Computational Mechanics

Salloum, Maher S.; Johnson, Kyle J.; Bishop, Joseph E.; Aytac, Jon M.; Dagel, Daryl D.; van Bloemen Waanders, Bart G.

New manufacturing technologies such as additive manufacturing require research and development to minimize the uncertainties in the produced parts. The research involves experimental measurements and large simulations, which result in huge quantities of data to store and analyze. We address this challenge by alleviating the data storage requirements using lossy data compression. We select wavelet bases as the mathematical tool for compression. Unlike images, additive manufacturing data is often represented on irregular geometries and unstructured meshes. Thus, we use Alpert tree-wavelets as bases for our data compression method. We first analyze different basis functions for the wavelets and find the one that results in maximal compression and miminal error in the reconstructed data. We then devise a new adaptive thresholding method that is data-agnostic and allows a priori estimation of the reconstruction error. Finally, we propose metrics to quantify the global and local errors in the reconstructed data. One of the error metrics addresses the preservation of physical constraints in reconstructed data fields, such as divergence-free stress field in structural simulations. While our compression and decompression method is general, we apply it to both experimental and computational data obtained from measurements and thermal/structural modeling of the sintering of a hollow cylinder from metal powders using a Laser Engineered Net Shape process. The results show that monomials achieve optimal compression performance when used as wavelet bases. The new thresholding method results in compression ratios that are two to seven times larger than the ones obtained with commonly used thresholds. Overall, adaptive Alpert tree-wavelets can achieve compression ratios between one and three orders of magnitude depending on the features in the data that are required to preserve. These results show that Alpert tree-wavelet compression is a viable and promising technique to reduce the size of large data structures found in both experiments and simulations.

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.

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

Abstract not provided.

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.

Jared, Bradley H.; Moore, David G.; Stair, Sarah L.; Boyce, Brad B.; Madison, Jonathan D.; Ivanoff, Thomas I.; Ostien, Jakob O.; Johnson, Kyle J.; Rodelas, Jeffrey R.; Salzbrenner, Bradley S.; Swiler, Laura P.; Jackson, Olivia D.; Saiz, David J.; Kernen, Burke L.; Jost, Elliott W.; Miers, John C.

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.

Salloum, Maher S.; Johnson, Kyle J.; Bishop, Joseph E.; van Bloemen Waanders, Bart G.; Karlson, Kyle N.; Beresh, Steven J.

Abstract not provided.

Swiler, Laura P.; Swiler, Laura P.; Johnson, Kyle J.

Abstract not provided.

Swiler, Laura P.; Johnson, Kyle J.

Abstract not provided.

Rodgers, Theron R.; Johnson, Kyle J.; Brown, Judith A.; Abdeljawad, Fadi F.; Madison, Jonathan D.; Mitchell, John A.; Tikare, Veena T.

Abstract not provided.