Lawrence Livermore National Laboratory



This crosscutting task focuses on the intelligent design of process parameters for experiments and simulations, including both the powder and effective medium models. The goal is to reduce the costs by running a small number of expensive experiments and simulations while maximizing the insight gained from them so we can quickly identify the process necessary to build a complex part with desired properties. The experiments will include single tracks, multiple tracks, multiple layers, density pillars, tensile samples, residual stress samples, lattices, thin walls, and other representative geometrical shapes determined by the part to be built. The data from both simulations and experiments will be analyzed to gain insight. This will also include uncertainty analyses, as well as risk and reliability analyses, to support qualification and certification of AM parts. (Kamath et al: 2014)

Approach

(Kamath et al: 2014)

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

Click for Design and Analysis of Simulation and Experiments

(Kamath: 2016)

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