Publications

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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.

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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 computa- tional 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, recrystal- lization, 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.

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