SBIR/STTR Award attributes
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#39;s multi-sensor data collection and analytics suite (AMSENSEreg;) 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.
