Publications

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Researchers review the challenges and opportunities that we are facing in the modeling and simulation of additive manufacturing processes for metals and the predictive representation of their mechanical performance at the different scales. They highlight the current modeling efforts taking place at the US Department of Energy National Nuclear Security Administration (NNSA) Laboratories, such as process modeling, microstructure modeling, properties modeling, performance and topology and process optimization. All these various modeling developments at different scales and regimes are necessary to move toward an integrated computational approach of process-structure-properties-performance that will ultimately enable the engineering and optimization of materials to specific performance requirements. Truchas, a continuum thermo-mechanical modeling tool originally designed for the simulation of casting processes, is being extended to simulate directed energy deposition additive manufacturing processes.

Additive manufacturing (AM) is of tremendous interest given its ability to realize complex, non-traditional geometries in engineered structural materials. However, microstructures generated from AM processes can be equally, if not more, complex than their conventionally processed counterparts. While some microstructural features observed in AM may also occur in more traditional solidification processes, the introduction of spatially and temporally mobile heat sources can result in significant microstructural heterogeneity. While grain size and shape in metal AM structures are understood to be highly dependent on both local and global temperature profiles, the exact form of this relation is not well understood. Here, an idealized molten zone and temperature-dependent grain boundary mobility are implemented in a kinetic Monte Carlo model to predict three-dimensional grain structure in additively manufactured metals. To demonstrate the flexibility of the model, synthetic microstructures are generated under conditions mimicking relatively diverse experimental results present in the literature. Simulated microstructures are then qualitatively and quantitatively compared to their experimental complements and are shown to be in good agreement.

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A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. This workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. Methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. Additionally, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

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The purpose of this document is to define the rules of governance for the Sandia Postdoctoral Development (SPD) Association. This includes election procedures for filling vacancies on the SPD board, an all-purpose voting procedure, and definitions for the roles and responsibilities of each SPD board member. The voting procedures can also be used to amend the by-laws, as well as to create, dissolve, or consolidate vacant SPD board positions.

The SNL SPD Association represents all personnel that are classified as Postdoctoral Appointees at Sandia National Laboratories. The purpose of the SNL SPD Association is to address the needs and concerns of Postdoctoral Appointees within Sandia National Laboratories.

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Using the kinetic Monte Carlo simulator, Stochastic Parallel PARticle Kinetic Simulator, from Sandia National Laboratories, a user routine has been developed to simulate mesoscale predictions of a grain structure near a moving heat source. Here, we demonstrate the use of this user routine to produce voxelized, synthetic, three-dimensional microstructures for electron-beam welding by comparing them with experimentally produced microstructures. When simulation input parameters are matched to experimental process parameters, qualitative and quantitative agreement for both grain size and grain morphology are achieved. The method is capable of simulating both single- and multipass welds. As a result, the simulations provide an opportunity for not only accelerated design but also the integration of simulation and experiments in design such that simulations can receive parameter bounds from experiments and, in turn, provide predictions of a resultant microstructure.