Application of the fog computing paradigm to additive manufacturing process monitoring and control

Abstract

Monitoring and controlling Additive Manufacturing (AM) processes play a critical role in enabling the production of quality parts. AM processes generate large volumes of structured and unstructured in-situ measurement data. The ability to analyze this volume and variety of data in real-time is necessary for effective closed-loop control and decision-making. Existing control architectures are unable to handle this level of data volume and speed. This paper investigates the functional and computational requirements for real-time closed-loop AM process control. The paper uses those requirements to propose a function architecture for AM process monitoring and control. That architecture leads to a fog-computing solution to address the big data and real-time control challenges.