| Abstract Scope |
This work is an application of data science to reduce cost of producing quality pre-production parts as per the NASA specification MSFC-SPEC-3717, which describes the development of a Qualified Metallurgical Process (QMP) and a Qualified Part Process (QPP). QMP demonstrates ability to produce a set of reference parts, which are designed to push the limits of the laser powder bed process. QMP creates a baseline for QPP, i.e., a pre-qualification of the AM process for QPP. The data management for QMP is as per the FAIR (Findable, Accessible, Interoperable and Reusable) principles using Common Data Dictionary (CDD), Common Data Model (CDM), and Common Data Exchange Format (CDEF).
We measure process variability across the build platform due to gas flow and laser caustic, develop process parameters for QMP using physics-based modeling, and perform data management using FAIR principles to demonstrate robustness of process parameters and estimate cost savings for QPP. |