Design and testing of a digital twin for monitoring and quality assessment of material extrusion process

In this paper are illustrated conception, realization and validation of an original solution for the digital twin of a material extrusion 3D printer, the most popular additive manufacturing machine. The system is composed by three main modules: a core containing the simulation engine, a data interface managing incoming data and a graphical interface enabling user remote control. It receives as input the process data collected by several sensors and the same part program (G-Code file) used by the real machine; thus, the system provides various real time functions for process monitoring, condition monitoring and geometrical accuracy control. Alongside detecting load on critical components and checking wear and tear, it can also systematically sort data collected or calculated during operation to help in optimizing printing parameters. Through interaction with the print host software, the twin is able to intervene directly in the current process to pause printing in case of anomalies and to assist users along a recovery procedure. An index of quality of the printed piece is obtained by comparing the CAD model of the printed part and a 3D model of the deposited material powered by the data coming from the machine. The system has been tested on a custom-made Cartesian printer: a number of prints were made with different speeds and accelerations to assess the impact of these settings on the average quality, and a more in-depth study was carried out on the digital models of the prints to investigate the origin of the defects detected. The programs and devices used do not rely on commercial solutions, so that the system is easily replicable. © 2022 Elsevier B.V.