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.

Boyce, Brad B.; Garland, Anthony G.; White, Benjamin C.; Jared, Bradley H.; Conway, Kaitlynn C.; Adstedt, Katerina A.; Dingreville, Remi P.; Robbins, Joshua R.; Walsh, Timothy W.; Alvis, Timothy A.; Branch, Brittany A.; Kaehr, Bryan J.; Kunka, Cody; Leathe, Nicholas L.

With the rapid proliferation of additive manufacturing and 3D printing technologies, architected cellular solids including truss-like 3D lattice topologies offer the opportunity to program the effective material response through topological design at the mesoscale. The present report summarizes several of the key findings from a 3-year Laboratory Directed Research and Development Program. The program set out to explore novel lattice topologies that can be designed to control, redirect, or dissipate energy from one or multiple insult environments relevant to Sandia missions, including crush, shock/impact, vibration, thermal, etc. In the first 4 sections, we document four novel lattice topologies stemming from this study: coulombic lattices, multi-morphology lattices, interpenetrating lattices, and pore-modified gyroid cellular solids, each with unique properties that had not been achieved by existing cellular/lattice metamaterials. The fifth section explores how unintentional lattice imperfections stemming from the manufacturing process, primarily sur face roughness in the case of laser powder bed fusion, serve to cause stochastic response but that in some cases such as elastic response the stochastic behavior is homogenized through the adoption of lattices. In the sixth section we explore a novel neural network screening process that allows such stocastic variability to be predicted. In the last three sections, we explore considerations of computational design of lattices. Specifically, in section 7 using a novel generative optimization scheme to design novel pareto-optimal lattices for multi-objective environments. In section 8, we use computational design to optimize a metallic lattice structure to absorb impact energy for a 1000 ft/s impact. And in section 9, we develop a modified micromorphic continuum model to solve wave propagation problems in lattices efficiently.

Additive Manufacturing

Rodgers, Theron R.; Moser, Daniel M.; Abdeljawad, Fadi; Jackson, Olivia D.; Carroll, Jay D.; Jared, Bradley H.; Bolintineanu, Dan S.; Mitchell, John A.; Madison, Jonathan D.

Grain-scale microstructure evolution during additive manufacturing is a complex physical process. As with traditional solidification methods of material processing (e.g. casting and welding), microstructural properties are highly dependent on the solidification conditions involved. Additive manufacturing processes however, incorporate additional complexity such as remelting, and solid-state evolution caused by subsequent heat source passes and by holding the entire build at moderately high temperatures during a build. We present a three-dimensional model that simulates both solidification and solid-state evolution phenomena using stochastic Monte Carlo and Potts Monte Carlo methods. The model also incorporates a finite-difference based thermal conduction solver to create a fully integrated microstructural prediction tool. The three modeling methods and their coupling are described and demonstrated for a model study of laser powder-bed fusion of 300-series stainless steel. The investigation demonstrates a novel correlation between the mean number of remelting cycles experienced during a build, and the resulting columnar grain sizes.

Robinson, David R.; Salloum, Maher S.; Ridzal, Denis R.; Kouri, Drew P.; Saiz, David J.; Jared, Bradley H.

Abstract not provided.

Jared, Bradley H.; Damm, David L.; Stacy, Shawn C.; Saiz, David J.; Moore, David G.; Epstein, Collin E.; Boyce, Brad B.; Jensen, Scott C.; McCarthy, Riley M.; Alberdi, Ryan A.; Heiden, Michael J.; Robbins, Joshua R.; Ruggles, Timothy R.; Branch, Brittany A.

Abstract not provided.

Materials Science and Engineering: A

Heckman, Nathan H.; Ivanoff, Thomas I.; Roach, Ashley M.; Jared, Bradley H.; Tung, Daniel J.; Brown-Shaklee, Harlan J.; Huber, Todd H.; Saiz, David J.; Koepke, Joshua R.; Rodelas, Jeffrey R.; Madison, Jonathan D.; Salzbrenner, Bradley S.; Swiler, Laura P.; Jones, Reese E.; Boyce, Brad B.

The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. It is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.

Salloum, Maher S.; Robinson, David R.; Ridzal, Denis R.; Kouri, Drew P.; Saiz, David J.; Jared, Bradley H.

Abstract not provided.

Jared, Bradley H.; Madison, Jonathan D.; Saiz, David J.; Mitchell, John A.; Koepke, Joshua R.; Ivanoff, Thomas I.

Abstract not provided.

Additive Manufacturing

Mitchell, John A.; Ivanoff, Thomas I.; Dagel, Daryl; Madison, Jonathan D.; Jared, Bradley H.

Porosity in additively manufactured metals can reduce material strength and is generally undesirable. Although studies have shown relationships between process parameters and porosity, monitoring strategies for defect detection and pore formation are still needed. In this paper, instantaneous anomalous conditions are detected in-situ via pyrometry during laser powder bed fusion additive manufacturing and correlated with voids observed using post-build micro-computed tomography. Large two-color pyrometry data sets were used to estimate instantaneous temperatures, melt pool orientations and aspect ratios. Machine learning algorithms were then applied to processed pyrometry data to detect outlier images and conditions. It is shown that melt pool outliers are good predictors of voids observed post-build. With this approach, real time process monitoring can be incorporated into systems to detect defect and void formation. Alternatively, using the methodology presented here, pyrometry data can be post processed for porosity assessment.

Jared, Bradley H.; Madison, Jonathan D.; Mitchell, John A.; Saiz, David J.; Koepke, Joshua R.; Dagel, Daryl D.; Ivanoff, Thomas I.

Abstract not provided.

Ivanoff, Thomas I.; Madison, Jonathan D.; Mitchell, John A.; Dagel, Daryl D.; Jared, Bradley H.

Abstract not provided.

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

Abstract not provided.

Jared, Bradley H.; Mitchell, John A.; Ivanoff, Thomas I.; Saiz, David J.; Koepke, Joshua R.; Madison, Jonathan D.; Swiler, Laura P.

Abstract not provided.

Madison, Jonathan D.; Ivanoff, Thomas I.; Mitchell, John A.; Dagel, Daryl D.; Koepke, Joshua R.; Saiz, David J.; Jared, Bradley H.

Abstract not provided.

Mitchell, John A.; Ivanoff, Thomas I.; Dagel, Daryl D.; Jared, Bradley H.; Koepke, Joshua R.; Saiz, David J.; Swiler, Laura P.

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.

Jared, Bradley H.; Boyce, Brad B.; Madison, Jonathan D.; Rodelas, Jeffrey R.; Saiz, David J.; Koepke, Joshua R.; Huber, Todd H.; Ivanoff, Thomas I.; Salzbrenner, Bradley S.; Kernen, Burke L.; Moore, David G.; Swiler, Laura P.

Abstract not provided.

Mitchell, John A.; Ivanoff, Thomas I.; Madison, Jonathan D.; Dagel, Daryl D.; Jared, Bradley H.

Abstract not provided.

Jared, Bradley H.; Madison, Jonathan D.; Ivanoff, Thomas I.; Swiler, Laura P.; Saiz, David J.; Dagel, Daryl D.; Mitchell, John A.; Koepke, Joshua R.

Abstract not provided.

Madison, Jonathan D.; Dagel, Daryl D.; Ivanoff, Thomas I.; Jared, Bradley H.; Koepke, Joshua R.; Mitchell, John A.; Saiz, David J.; Swiler, Laura P.

Abstract not provided.

Ivanoff, Thomas I.; Madison, Jonathan D.; Kramer, Sharlotte L.; Swiler, Laura P.; Jared, Bradley H.

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.

Ivanoff, Thomas I.; Madison, Jonathan D.; Koepke, Joshua R.; Swaller, Erich S.; Jared, Bradley H.; Mitchell, John A.; Swiler, Laura P.

Abstract not provided.

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.

Koepke, Joshua R.; Jared, Bradley H.; Saiz, David J.; Hill, Ryan H.; Taylor, Samantha T.; Swiler, Laura P.

Abstract not provided.