Lawrence Livermore National Laboratory

We are employing modeling and simulation, process optimization, design of simulations and experiments, data mining, and uncertainty analysis as part of our AM materials strategy. We have an effective medium simulation that models the process at the scale of the part and a mesoscale simulation that models the process at the scale of the powder. The powder model feeds information to the effective medium model that is not obtainable via experiment. The incorporation of process optimization, data mining, and uncertainty analysis will guide the AM process to yield optimized properties and performance. Results of simulations will be validated against measured material properties and data acquired from real-time in-situ process monitors.

The AM process is rich with scientific challenges including absorption of energy from laser or electron beams, melting and solidification, solidification shrinkage, phase transformations, phase separation, microstructural evolution, convection, heat conduction, radiative losses, wetting and dewetting, sintering, Marangoni convection, capillary forces, and vaporization. This project will advance the field of additive manufacturing by:

  • Developing feed forward algorithms to achieve "right first time, right every time" procesing.
  • Developing predictive process-structure-property relationships integrated with the AM process
  • Developing a thorough understanding of the basic physics of AM processes to capture the complexity in the multiple interacting physical phenomena
  • Developing sufficient understanding of processes to be able to prescribe processing conditions and achieve the desired properties in 1 or 2 attempts
  • Developing new sensors that can operate in build-chamber environments and can be used to feedback to the process

Essential elements


We are an integrated, multidisciplinary team focused on developing the tools that will lead to acceleration of the qualification and certification of additively manufactured metal components through:

Physics based models – Developing physics based models that relate microstructure, properties, and process (including post processing) to performance and are foundational to process control and certification.
Validating Experiments – Providing validating experiments as prescribed by Design of Experiments; gaining scientific insights into simulations and experiments through data mining; and understanding how uncertainties in inputs influence the outputs by using UQ
Integrated Sensing and Control – Developing integrated in-process sensing, monitoring, and control technologies to ensure the end-processed material properties and component performance
New Processes – Developing new processes to improve quality, certifiability, and speed of additive manufacturing

Watch a technical presentation summarizing ACAMM by Wayne King at the National Academy of Sciences Workshop on Predictive Theoretical and Computational Approaches for Additive Manufacturing that was held Wednesday, October 7 and Thursday, October 8, 2015.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.