Physics-Based Selection of SLM Process Parameters to Mitigate Defects and to Control Deposit Microstructure

Status: Completed

Start Date: 2014-06-20

End Date: 2014-12-19

Description: The research objectives of this proposal are to: (1) To adapt the thermal-fluid science procedures for the prediction of weld defects to the prediction and control of surface defects and melt pool instability during the deposition of single tracks in SLM and to control the as-deposited microstructure. (2) Utilize physics-based analysis to predict variability caused in the individual SLM track cross-section geometry due to the statistical distribution of powder particles sizes and the potential non-uniform placement of powder particles during recoating. This will also include performing statistical analysis of the variability and to develop a probabilistic model to calculate levels of confidence and exceedance for the size and type of potential defects as a function of the SLM process parameters. (3) Determine the thermal cycling during deposition and use it to predict solidification microstructure and solid state transformations during deposition; and (4) Demonstrate feasibility of the analytical procedures for alloy IN718.
Benefits: The NASA application is to enable physics-based selection of SLM process parameters to mitigate defects and to control microstructure for a variety of aerospace materials. It is designed to reduce the effect needed to fulfill the certification requirements for parts manufactured for NASA. This application is needed because the commercially available SLM systems are based on parameters designed by trial and error, and only for a few alloys and powders. This work is expected to reduce this effort to design an SLM process for new alloy by a factor of ten.

Physics-based selection of SLM process parameters to mitigate defects and to control microstructure for a variety of materials utilized in land or sea-based gas-turbine engines, for life-extension of aging systems, repair of jet engine components for commercial aircraft, etc.

Lead Organization: Applied Optimization, Inc.