Publications

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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.

We propose a reformulation of the composite tetrahedral finite element first introduced by Thoutireddy et al. By choosing a different numerical integration scheme, we obtain an element that is more accurate than the one proposed in the original formulation. We also show that in the context of Lagrangian approaches, the gradient and projection operators derived from the element reformulation admit fully analytic expressions, which offer a significant improvement in terms of accuracy and computational expense. For plasticity applications, a mean-dilatation approach on top of the underlying Hu–Washizu variational principle proves effective for the representation of isochoric deformations. The performance of the reformulated element is demonstrated by hyperelastic and inelastic calculations. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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The second Sandia Fracture Challenge illustrates that predicting the ductile fracture of Ti-6Al-4V subjected to moderate and elevated rates of loading requires thermomechanical coupling, elasto-thermo-poro-viscoplastic constitutive models with the physics of anisotropy and regularized numerical methods for crack initiation and propagation. We detail our initial approach with an emphasis on iterative calibration and systematically increasing complexity to accommodate anisotropy in the context of an isotropic material model. Blind predictions illustrate strengths and weaknesses of our initial approach. We then revisit our findings to illustrate the importance of including anisotropy in the failure process. Mesh-independent solutions of continuum damage models having both isotropic and anisotropic yields surfaces are obtained through nonlocality and localization elements.

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in (Formula presented.) 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.

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Recent work at Sandia National Laboratories has focused on preparing strong predictive models for the simulation of ductile failure in metals. The focus of this talk is on the development of engineering-ready models that use a phenomenological approach to represent the ductile fracture processes. As such, an empirical tearing parameter that accounts for mean stress effects along the crack front is presented. A critical value of the tearing parameter is used in finite element calculations as the criterion for crack growth. Regularization is achieved with three different methods and the results are compared. In the first method, upon reaching the critical tearing, the stress within a solid element is decayed by uniformly shrinking the yield surface over a user specified amount of strain. This yields mesh-size dependent results. As a second method for regularization, cohesive surface elements are inserted using an automatic remeshing technique. In the third method, strain-localization elements are inserted with the automated remeshing.