This paper focuses on a method to integrate additive manufacturing (AM) structure, processing, and property-geometry modeling methods to facilitate the qualification and certification of AM-fabricated metal parts and enable their rapid deployment. The conventional approach to qualifying AM parts is to destructively evaluate a significant number of parts, measure the properties of interest, and look for anomalies in each part. This approach also requires statistical sampling of parts for destructive testing to verify the process is still operating correctly over time. This is costly, time consuming, and negates much of the benefits offered by AM. The approach outlined in this paper leverages OpenMETA, a suite of tools that provide a unified design space, a unified system representation across engineering specialties, and multidisciplinary workflow for optimization, design-of-experiments, and trade-off studies. OpenMETA provides the ability to conduct high-fidelity, low-fidelity, and hybrid analyses within one framework. Test benches are built within OpenMETA that capture the requirements for the component in an executable manner to support automated analysis such as thermal analysis, finite element analysis, and high-fidelity physics-based (HFPB) analysis. Test benches also describe the intended environment in which the component will be used after manufacture. This description of the environment might include details of the surrounding components, the interfaces to those components, and corrosive agents. Test bench results are used to estimate and assess component performance related to requirements. This paper focuses on one technology for manufacturing the parts, a wire-fed, robotic, pulsed-arc AM process, although the OpenMETA platform can be applied to other AM technologies.