5567.004 AM Process Pre-Qualification

Multi-scale, multi-physics simulations to predict AM process parameters, accounting for the residual heat buildup, local thermal behavior, and melt pool physics. These simulations are aligned with the NASA standard to mitigate defects and produce consistent solidification profiles for the as-built material.


NASA-STD-6030 and NASA MSFC-SPEC-3717 define process control qualification requirements (PCQR) to determine a qualified material process (QMP) for laser powder bed fusion (LPBF). The PCQR seek consistent defect-free material, melt pool morphology, solidification conditions, and surface roughness.


The objective is to demonstrate a reusable method to reduce the cost of LPBF process qualification by up to 40% for the pre-qualification of the LPBF process and develop a QMP to produce a set of reference parts as per PCQR defined by the NASA standards. Due to gas flow and laser caustic and fused with model-based data to explore the processing envelope and understand the sensitivity of microstructure and flaw state to process parameters variability will be measured across the LPBF build platform. This effort will also demonstrate a reduction in the non-recurring engineering (NRE) expenses incurred to develop QMP for reference parts.

Technical Approach

Led by Applied Optimization (AO), the team will perform experimental gas flow and laser caustic measurements across the build platform. AO will account for variability in the LPBF process simulations and fuse the simulation results with measured data to assess the effects of build platform location and spatter on the material flaw state, microstructure, and surface finish. Using these results, the project team will create reusable strategies to account for variances to eliminate the need for repeated build experiments. These strategies will be used to demonstrate reduced NRE expenses to qualify parts using a set of reference parts defined by Lockheed Martin (LM). LM will define reference parts to push the limits of the LPBF process, e.g., using geometry and skin features for the parts of interest. Next, AO will predict the laser and machine settings used to control the LPBF process for these reference objects to produce consistent as-built material. Once machine setting control predictions are applied, LM will produce the builds and assess the material consistency and flaw state. Finally, AO will refine the laser and machine settings to correct anomalies in the as-built material and enhance the tolerance for build-to-build process variation.

Project Participants

Project Principal

Other Project Participants

  • American Society for Testing and Materials (ASTM)
  • Lockheed Martin

Public Participants

  • U.S. Department of Defense

Project Summary

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