Ontology NetworkBased InSitu Sensor Selection for Quality Management in Metal Additive Manufacturing

Abstract

Metal additive manufacturing (MAM) offers a larger design space with greater manufacturability than traditional manufacturing. Despite continued advances, MAM processes still face huge uncertainty, resulting in variable part quality. Realtime sensing for MAM processing helps quantify uncertainty by detecting build failure and process anomalies. While the high volume of multidimensional sensor data—such as meltpool geometries and temperature gradients—is beginning to be explored, sensor selection does not yet effectively link sensor data to part quality. To begin investigating such connections, we propose networkbased models that capture in realtime (1) sensor data's association with process variables and (2) asbuilt part qualities’ association with related physical phenomena. These sensor models and networks lay the foundation for a comprehensive framework to monitor and manage the quality of MAM process outcomes.