NASA intends to implement components built by additive manufacturing (AM) processes into space-flight systems. To achieve this goal, NASA requires a deeper understanding of the AM process, particularly for current alloy systems and existing AM manufacturing equipment. As a continuation of earlier internal research and development work at SwRI step towards an integrated computational materials engineering (ICME) framework, we propose to develop an extensible framework of the AM process to provide Rapid Additive Manufacturing Build Outcomes - RAMBO. RAMBO will provide thermal histories, microstructure evolution, and material properties of an additively manufactured part built using the powder bed fusion process. We will develop fast-running routines to provide thermal histories during the build process. The thermal routines drive predictions of microstructural features, such as average grain size, material phase fraction, and lack-of-fusion defects. Microstructural features would ultimately in turn drive predictions of mechanical properties (e.g., stress-strain curves) for the additively manufactured material. The focused material system for this initial software development effort will be Ti-6Al-4V. By defining I/O through common interfaces, the RAMBO framework enforces modularity of the software components. As a result, RAMBO will readily accommodate additional physics, new material systems, and novel manufacturing processes provided that the underlying routines conform to the common interface. This software framework would support NASA’s “Vision 2040” to provide robust, interoperable, adaptive, and accessible methods.
Possible NASA applications include: Space flight systems using components built by AM processes include MOXIE, SHERLOC, ion engines, and other spacecraft structural and multi-functional applications. These components would benefit from computationally inexpensive predictions of microstructural features, lack-of-fusion defects, and material properties. Predictions of defect distributions are especially important for high-criticality components under fatigue loading as they may support qualification by probabilistic fracture mechanics.
There is high-overlap between the non-NASA market for RAMBO and users of the DARWIN and NASGRO software products (e.g., AFRL, NAVAIR, aircraft engine OEMs). Efficient prediction of AM outcomes with RAMBO would enable sensitivity studies of AM process parameters on material features without testing on AM machines. Researchers could tailor parameters to optimize microstructure for their application.