The proposed innovation for this work is an efficient simulation software combined with in-situ sensing capability for use with laser powder bed fusion (LPBF) machines to detect defects before initiating the build; thus allowing abatement of the defects before they are materially created. The predictive thermal simulation capabilities developed by the University of Pittsburgh, to be combined with Open Additive's multi-sensor data collection and analytics suite (AMSENSE®) and transitioned into a commercial software framework, will provide a comprehensive solution for the development, validation, and transition of quality assurance strategies for additively manufactured metal parts for aerospace applications. The resulting toolset will provide an efficient simulate-before-build approach that will enable the ability to print low volume, highly critical complex geometric parts by LPBF at reduced timelines and cost compared to the current state of the art.
The proposed simulator in combination with AMSENSE sensing and analytics capabilities will provide a robust prediction and monitoring solution for low volume, highly critical parts. The effort will provide a method tosimulate-before-build for complex novel geometries to identify ideal laser processing parameters. This will accelerate the qualification of laser powder bed fusion (LPBF) processes and parts for use on NASA mission projects such as the Mars Oxygen In-Situ Resource Utilization Experiment (MOXIE) and other endeavors.
The proposed simulation tool combined with in-situ sensing/analytics will have wide applicability to defense and industrial needs for additively manufactured parts to support modernization and systems sustainment. The toolset will provide an integrated approach to reduce the costs and lead times involved in AM applications development, thus paving way for more materials and complex geometries.