A Centralized Framework for System-Level Control and Management of Additive Manufacturing Fleets

Abstract

In most industrial additive manufacturing (AM) applications a set of AM machines (AM-Fleet) are used in parallel. An AM-Fleet often consists of machines from various vendors and may include different AM processes. AM processes often suffer from poor repeatability within a single build, between builds on the same machine, and from machine to machine. AM's lack of robustness is often attributed to insufficient in-process monitoring and feedback control, as well as unknown modeling dynamics, and a lack of process standards. To effectively monitor and control AM-Fleets, system-level approaches must be devised. In this work, a centralized approach is proposed for the system-level control and management of AM-Fleets. Integrating such an approach has advantages in terms of system-level intelligent decision making for AM-Fleets. Key problems that needs to be solved and the challenges for a centralized approach are discussed in this work. The architecture of the proposed framework is presented with discussions on the individual components. A discrete event model for the system-level monitoring and control of AM machines is also proposed to support the presented framework. The use of discrete event models creates an abstract representation of the AM machine that enables the supervision of the physical system. An illustrative example that demonstrates a system-level run-to-run anomaly detection case is discussed. The proposed framework will provide an analytical foundation for systematic anomaly detection, scheduling, and decision making in AM-Fleets.