Lawrence Livermore National Laboratory

This modeling and simulation development approach is built upon the technical foundation that has already been established for simulation of manufacturing processes, e.g., LLNL tools and models have been developed and used to capture the forming, rolling and casting of complex shaped parts in collaboration with Alcoa. (Karabin et al.: 2003)

Click for schematic of modeling and simulation strategy

Process ModelRole
Intelligent feed forward Used to train the additive manufacturing machine to build a part right the first time.
Effective medium model Used to computationally build a complete part and predict properties such as residual stress, density, and strength in 3D. Treats powder as a lower density, low strength solid. Laser material interaction is treated using an energy source term. Models melting, solidification, and solid state phase transformations. Includes materials strength. Covers time scale to hours and length scales to centimeters.
Powder model Used to computationally model the melting of powder and its resulting densification to improve the AM process and provide data to the effective medium model. Resolves individual powder particles in 3D. Laser–material interaction is treated via ray tracing and physics-based absorption model. Models melting of the powder, flow of the liquid, and behavior of trapped gas. Covers time scales on the order of fractions of a second and length scales of fractions of a millimeter.
Ultrafast Surrogate Models For Process Optimization, Uncertainty Analysis, And Sensitivity Assessment Used to develop models that are computationally efficient and can be run in the context of an optimization.
Accelerating Characterization of Parts and Powder Used to shorten the time necessary to characterize materials made by additive manufacturing
Design and analysis of simulations and experiments Used to make the most efficient choice of simulation and experiment.

"New physics-based models of AM processes are needed to understand and predict material properties such as surface roughness and fatigue. A better understanding of the basic physics could then potentially lead to predictive modeling, allowing designers, engineers, scientists, and users to estimate the functional properties of the part during design and tweak the design to achieve desired outcome."

(Scott et al.: 2012)