Publications

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This work proposes a finite element (FE) analysis workflow to simulate directed energy deposition (DED) additive manufacturing at a macroscopic length scale (i.e. part length scale) and to predict thermal conditions during manufacturing, as well as distortions, strength and residual stresses at the completion of manufacturing. The proposed analysis method incorporates a multi-step FE workflow to elucidate the thermal and mechanical responses in laser engineered net shaping (LENS) manufacturing. For each time step, a thermal element activation scheme captures the material deposition process. Then, activated elements and their associated geometry are analyzed first thermally for heat flow due to radiation, convection, and conduction, and then mechanically for the resulting stresses, displacements, and material property evolution. Simulations agree with experimentally measured in situ thermal measurements for simple cylindrical build geometries, as well as general trends of local hardness distribution and plastic strain accumulation (represented by relative distribution of geometrically necessary dislocations).

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Additive manufacturing offers unprecedented opportunities to design complex structures optimized for performance envelopes inaccessible under conventional manufacturing constraints. Additive processes also promote realization of engineered materials with microstructures and properties that are impossible via traditional synthesis techniques. Enthused by these capabilities, optimization design tools have experienced a recent revival. The current capabilities of additive processes and optimization tools are summarized briefly, while an emerging opportunity is discussed to achieve a holistic design paradigm whereby computational tools are integrated with stochastic process and material awareness to enable the concurrent optimization of design topologies, material constructs and fabrication processes.

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Laser engineered net shaping (LENS) is an additive manufacturing process that presents a promising method of creating or repairing metal parts not previously feasible with traditional manufacturing methods. The LENS process involves the directed deposition of metal via a laser power source and a spray of metal powder co-located to create and feed a molten pool (also referred to generically as Directed Energy Deposition, DED). DED technologies are being developed for use in prototyping, repair, and manufacturing across a wide variety of materials including stainless steel, titanium, tungsten carbidecobalt, aluminum, and nickel based superalloys. However, barriers to the successful production and qualification of LENS produced or repaired parts remain. This work proposes a finite element (FE) analysis methodology capable of simulating the LENS process at the continuum length scale (i.e. part length scale). This method incorporates an element activation scheme wherein only elements that exceed the material melt temperature during laser heating are activated and carried through to subsequent analysis steps. Following the initial element activation calculation, newly deposited, or activated elements and the associated geometry, are carried through to thermal and mechanical analyses to calculate heat flow due to radiation, convection, and conduction as well as stresses and displacements. The final aim of this work is to develop a validated LENS process simulation capability that can accurately predict temperature history, final part shape, distribution of strength, microstructural properties, and residual stresses based on LENS process parameters.

This report documents completion of a Level 2 Milestone on the development of a multi-physics capability to predict the evolving material state through the manufacturing process of a Gas Transfer Systems (GTS) reservoir. We present details on new developments and capability improvements that address the following completion criteria: (i) validation of a microstructure evolution model, including recrystallization and strain aging, (ii) demonstration of the capability to remesh, map and transfer material state (internal state variables) and residual stress from forging to machining to welding processes, and (iii) formal V&V characterization and quantification of uncertainties of material parameters and manufacturing process parameters on residual stress.

Additive Manufacturing (AM) provides a new avenue to design innovative materials and components that cannot be created using traditional machining operations. With current AM capabilities, complex designs (such as those required in weapon systems) can be readily manufactured with laser powder forming (or Laser-Engineered Net Shaping (LENSTM)) [1] that would be otherwise cost prohibitive or impossible to produce. However, before an AM product can be qualified for weapon applications, the characteristics of the metals produced by additive manufacturing processes need to be well understood. This work focuses on the development of computational simulation tools to model the metal additive manufacturing process. This work extends and integrates existing Sandia National Laboratories tools to accomplish the following: (i) be able to better predict residual stresses in AM product, (ii) extend high-fidelity material models to capture material evolution during the formation process, leading to prediction of end-state material properties, and (iii) provide a basis for engineering tools to propose improvements to additive manufacturing process variables, including those that minimize process variation. While this work in its current state is directly applicable to additive manufacturing processes, the tools developed may also help enable modeling welding processes such as gas tungsten arc (GTA), electron beam, and laser welding.

Residual stresses induced during forging and welding can cause detrimental failure in reservoirs due to enhanced possibility of crack propagation. Therefore, reservoirs must be designed with yield strengths in a tight range. This report summarizes an effort to verify and validate a computational tool that was developed to aid in prediction of the evolution of residual stresses throughout the manufacturing process. The application requirements are identified and summarized in the context of the Predictive Capability Maturity Model (PCMM). The phenomena of interest that the model attempts to capture are discussed and prioritized using the Phenomena Identification and Ranking Table (PIRT) to identify any gaps in our approach. The fidelity of the modeling approach is outlined and details on the implementation and boundary conditions are provided. The code verification requirements are discussed and solution verification is performed, including a mesh convergence study on the series of modeling steps (forging, machining and welding). Validation activities are summarized, including validation of the displacements, residual stresses, recrystallization, yield strength and thermal history. A sensitivity analysis and uncertainty quantification are also performed to understand how variations in the manufacturing process affect the residual stresses.

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We are developing the capability to track material changes through numerous possible steps of the manufacturing process, such as forging, machining, and welding. In this work, experimental and modeling results are presented for a multiple-step process in which an ingot of stainless steel 304L is forged at high temperature, then machined into a thin slice, and finally subjected to an autogenous GTA weld. The predictions of temperature, yield stress, and recrystallized volume fraction are compared to experimental results.

The process of verification and validation can be resource intensive. From the computational model perspective, the resource demand typically arises from long simulation run times on multiple cores coupled with the need to characterize and propagate uncertainties. In addition, predictive computations performed for safety and reliability analyses have similar resource requirements. For this reason, there is a tradeoff between the time required to complete the requisite studies and the fidelity or accuracy of the results that can be obtained. At a high level, our approach is cast within a validation hierarchy that provides a framework in which we perform sensitivity analysis, model calibration, model validation, and prediction. The evidence gathered as part of these activities is mapped into the Predictive Capability Maturity Model to assess credibility of the model used for the reliability predictions. With regard to specific technical aspects of our analysis, we employ surrogate-based methods, primarily based on polynomial chaos expansions and Gaussian processes, for model calibration, sensitivity analysis, and uncertainty quantification in order to reduce the number of simulations that must be done. The goal is to tip the tradeoff balance to improving accuracy without increasing the computational demands.