Process monitoring of economic and environmental performance of a material extrusion printer using an augmented reality-based digital twin

Abstract

The concept of digital twin is based on the linkage of a physical system with a digital model-based representation and shows numerous potentials in additive manufacturing (AM). The existing approaches related to digital twins of AM mainly focus on the process, machine, and factory levels to monitor and optimize the process and system performance of AM. This study aims at the machine level and adopts augmented reality (AR) to develop a digital twin for a material extrusion printer. To provide a digital representation of the printing process of a component, this study has proposed an approach using cumulated small cylinders to approximate the geometry of the component, which is called “Volume Approximation by Cumulated Cylinders (VACCY)”. To monitor the economic and environmental performance, this study has modeled four process indicators (electricity use, manufacturing cost, greenhouse gas emission, and primary energy consumption) and integrated them in the AR-based digital twin. Through the test of the developed digital twin for printing three components, the feasibility and performance of the developed digital twin are well demonstrated.