Paper #4 – Spatiotemporal G-code Modeling for Secure FDM-based 3D Printing

3D printing constructs physical objects by building and stacking layers according to the CAD (Computer-aided Design) information. Attackers target a printing object by manipulating the printing parameters such as nozzle movement and temperature. The existing research on secure 3D printing mostly focuses on nozzle-kinetics, while attacks on filament-kinetics and thermodynamics can also damage the printed object. The detection of these attacks mainly relies on creating master-profile and machine learning by printing every unique object in a protected environment. In the fourth industrial revolution, such an approach is not suitable due to mass-customization rather than bulk production. This paper presents Sophos, a framework to detect nozzle-kinetic, filament-kinetic and thermodynamic attacks on the fused deposition modeling (FDM)-based 3D printing process. Sophos design does not require any prior learning for every unique object. It can detect the attacks on the first print using spatiotemporal G-code modeling, aligning it with the Industry 4.0 vision. Sophos is scalable and supports modular upgrades to suit different printing requirements. Its design allows the detection threshold to be reduced conveniently to as low as the 3D printer’s resolution, shifting the game to a more interesting study of attack patterns than attack magnitudes.