Recent advances in the industrial Internet of Things and Cyber–Physical Systems have resulted in greater connections and accessibility of smart manufacturing. A particularly exciting consequence is the development of a new paradigm of smart AM systems that seamlessly integrate computing, manufacturing, and services. Each individual AM or 3D printing machine in such a system enables direct manufacturing of complex shapes from CAD models with reduced labor and costs compared to traditional manufacturing methods. The impacts of such systems are not yet fully realized in practice because their constituent processes may yield inconsistent product quality. Furthermore, Individual processes and machines face varying degrees of insufficient data and physical knowledge. A specified process model through machine learning often has a limited scope of application across the vast spectrum of processes in a manufacturing system that are characterized by different settings of process variables, including lurking variables. Knowledge or model transfer among AM processes is therefore essential to smart AM systems.