Digital Data Management to Certify Additively Manufactured Parts with Reduced Inspection: AM Health Monitoring and Final Part Quality

The ability to effectively leverage additive manufacturing (AM) technologies hinges on quickly identifying, prioritizing, and communicating essential information required to manufacture parts. Capturing the right data during the manufacturing process may allow organizations to qualify structural parts for use straight from the manufacturing process, without the need for repetitive evaluation or lot acceptance testing. Currently the ability to generate, identify, prioritize, disseminate, reuse, and qualify AM data is challenging and time consuming.

Process qualification typically requires substantial non-recurring engineering (NRE) frequently requiring more than six months and $500,000 in cost. Advanced process qualification approaches, such as integrated computational materials engineering (ICME) and data-driven machine learning have been studied to reduce NRE. These techniques have shown promise; however, certifying bodies have yet to approve these methods as a means of process qualification.

LIFT, America Makes, and MxD, three DoD manufacturing innovation institutes, have executed a collaborative DoD-funded program to reduce cost and time required for AM qualification. The Collaboration Team used a multi-faceted approach which considered data sharing, security, and identification & prioritization of data types. Results from this initiative enable streamlining of the AM qualification process to get parts into the hands of the Warfighter sooner.

A stepwise approach was utilized to establish the infrastructure required for digital qualification processes. This presentation focuses on equipment health monitoring, dataset generation, and acceptance criteria development. The effects of variation in equipment health on quality was quantified across two thermal histories using thermal simulation to design coupons with different support features. Repeatable methods of varying equipment performance were established, a set of experiments was designed, and builds were performed. Coupons were thermally post-processed, machined, and mechanical testing was performed. Regression models were developed to link equipment health to part properties and thresholds for machine performance were defined.