a b s t r a c tThe fabrication of 3-D parts from CAD models by additive manufacturing (AM) is a disruptive technology that is transforming the metal manufacturing industry. The correlation between solidification microstructure and mechanical properties has been well understood in the casting and welding processes over the years. This paper focuses on extending these principles to additive manufacturing to understand the transient phenomena of repeated melting and solidification during electron beam powder melting process to achieve site-specific microstructure control within a fabricated component. In this paper, we have developed a novel melt scan strategy for electron beam melting of nickel-base superalloy (Inconel 718) and also analyzed 3-D heat transfer conditions using a parallel numerical solidification code (Truchas) developed at Los Alamos National Laboratory. The spatial and temporal variations of temperature gradient (G) and growth velocity (R) at the liquid-solid interface of the melt pool were calculated as a function of electron beam parameters. By manipulating the relative number of voxels that lie in the columnar or equiaxed region, the crystallographic texture of the components can be controlled to an extent. The analysis of the parameters provided optimum processing conditions that will result in columnar to equiaxed transition (CET) during the solidification. The results from the numerical simulations were validated by experimental processing and characterization thereby proving the potential of additive manufacturing process to achieve site-specific crystallographic texture control within a fabricated component.

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The paper presents the dynamic compound wavelet method (dCWM) for modeling the time evolution of multiscale and/or multiphysics systems via an "active" coupling of different simulation methods applied at their characteristic spatial and temporal scales. Key to this "predictive" approach is the dynamic updating of information from the different methods in order to adaptively and accurately capture the temporal behavior of the modeled system with higher efficiency than the (nondynamic) "corrective" compound wavelet matrix method (CWM), upon which the proposed method is based. The system is simulated by a sequence of temporal increments where the CWM solution on each increment is used as the initial conditions for the next. The numerous advantages of the dCWM method such as increased accuracy and computational efficiency in addition to a less-constrained and a significantly better exploration of phase space are demonstrated through an application to a multiscale and multiphysics reaction-diffusion process in a one-dimensional system modeled using stochastic and deterministic methods addressing microscopic and macroscopic scales, respectively.

The high computational cost involved in modeling of the progressive fracture simulations using large discrete lattice networks stems from the requirement to solve a new large set of linear equations every time a new lattice bond is broken. To address this problem, we propose an algorithm that combines the multiple-rank sparse Cholesky downdating algorithm with the rank-p inverse updating algorithm based on the Sherman-Morrison-Woodbury formula for the simulation of progressive fracture in disordered quasi-brittle materials using discrete lattice networks. Using the present algorithm, the computational complexity of solving the new set of linear equations after breaking a bond reduces to the same order as that of a simple backsolve (forward elimination and backward substitution) using the already LU factored matrix. That is, the computational cost is O(nnz(L)), where nnz(L) denotes the number of non-zeros of the Cholesky factorization L of the stiffness matrix A. This algorithm using the direct sparse solver is faster than the Fourier accelerated preconditioned conjugate gradient (PCG) iterative solvers, and eliminates the critical slowing down associated with the iterative solvers that is especially severe close to the critical points. Numerical results using random resistor networks substantiate the efficiency of the present algorithm.