Lawrence Livermore National Laboratory



Because of the chasm that exists between what we can simulate at high spatial resolution and the scale of a real part, high-speed surrogate models will be required. The ideas used in the field of DACE (Design and Analysis of Computer Experiments) involve sampling the input parameter space of the simulations and building surrogates that act as predictive models. If the sampling is adequate, the surrogates can be considered as providing reasonable approximations to the simulation output variables for a specific range of input parameters. These surrogates can be extremely useful in problems such as selective laser melting where the simulations are computationally very expensive.(Kamath: 2016)

Surrogates can be used to reduce the number of prototype runs needed to validate a given AM parameter set or material property. Surrogates can also be a tool to draw equivalence to the processes (e.g., machine to machine) and assist with predicting how configuration changes (e.g., machine upgrades) might affect the process-structure-property-performance relationships of the produced material. It can further help to identify and focus on the process parameters that are most important to the quality of the part and the ranges of those parameters where the sensitivity of the part quality to the parameter is minimized. The proper use of surrogates should increase confidence in individual component performance and decrease uncertainties in performance.

Modeling approach

Challenges addressed:

  • Accelerates process optimization to achieve desired properties and performance
  • Reduces or eliminates the need to "lock down" the process for the duration of a production cycle (establishing machine equivalence)
  • Achieving "right every time" production
  • Establishing a "digital thread" to accommodate and leverage the large amounts of data (including in situ sensor data) that come with the AM process

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