Metal additive manufacturing (MAM) provides a larger design space with accompanying manufacturability than traditional manufacturing. Recently, much research has focused on simulating the MAM process with regards to part geometry, porosity, and microstructure properties. Despite continued advances, MAM processes have many variables that are not well understood with respect to their effect on the part quality. With the common use of in-situ sensors — such as CMOS cameras and infrared cameras — numerous, real-time datasets can be captured and analyzed for monitoring both the process and the part.

However, currently, real-time data predominantly focuses on the build failure and process anomalies by capturing the printing defects (cracks/peel-off). A large amount of data — such as melt pool geometries and temperature gradients — are just beginning to be explored, along with their connections to final part quality. Towards investigating these connections, in this paper we propose models that capture numerous sensor capabilities and associate them with the corresponding, real-time, physical phenomena. These sensor models lay the foundation for a comprehensive, knowledge framework that forms the basis for quality monitoring and management of MAM process outcomes.

Using our previously developed process ontology model [1–3], which describes the relationship between process variables and process outcomes, we can discover the relationship between the real-time, physical phenomena and the deviations in the targeted, build quality. For example, statistically significant sensor data that predicts deviations from targeted process qualities can be detected and used to control the process parameters. Case studies that scope the physical phenomena and sensor data are provided for verifying the effectiveness and efficiency of the proposed qualification and certification models.

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