Abstract
AbstractMetal 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. Real-time sensing for MAM processing helps quantify uncertainty by detecting build failure and process anomalies. While the high volume of multidimensional sensor data—such as melt-pool 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 network-based models that capture in real-time (1) sensor data's association with process variables and (2) as-built 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.
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