4052 Digital Threading of AM

Additive manufacturing (AM) processes typically require multiple design and build iterations to successfully print the component to specification. These iterations can significantly impact processing costs and energy requirements. The ability to apply in situ monitoring during a build and linking that data to the digital thread has the potential to reduce or eliminate the iterations required for successful part creation by allowing digital information gained during the initial build(s) to be used to adjust build parameters or part files.

Three AM processes and vendors were analyzed in this project: fused deposition modeling (FDM) at Raytheon, laser freeform manufacturing technology (LFMT) at Boeing, and direct laser metal sintering (DMLS) at Aerojet Rocketdyne.  Each company provided notional part designs that contained geometry that had been challenging in legacy builds.  The parts were analyzed pre-build, in situ, and post build.

Pre-build analysis was completed utilizing CAD model quality (CADIQ) and healing tools (CADFix), provided by ITI, which provides an advanced starting point for the optimization of future build cycles, based upon the in situ and post build part analyses. Integrated thermal sensor packages, supplied and installed by Stratonics, were utilized to provide in situ process monitoring for real-time information and understanding of “good” vs. “bad” part builds through data capture of melt pool and global heat flow characteristics.  Post build geometry was analyzed using external geometry measurement and nondestructive computed tomography (CT) scan analysis of the built parts. The in situ data capture and post build analysis were then analyzed for anomalies and defects and overlaid onto the original part geometry to better understand defect conditions and patterns. The in situ and post build data was looped back into the pre-build analysis. The linkage between the build states (pre-build, in situ, and post build) was accomplished using the Linked Intelligent Master Model (LIMM) environment.

Additionally, the University of Tennessee at Knoxville (UTK) created an energy assessment tool that was integrated into the ITI software, also using the LIMM environment, which automated the creation of cost and lifecycle data associated with the build(s).  As an example, the assessment tool can be used to create a business case for AM with a comparison to conventional manufacturing methods by quantifying material energy, manufacturing energy, freight and distribution energy, use phase energy, and disposal energy for each process.