Spatiotemporal G-code Modeling for Additive Manufacturing Security
Northern Virginia Node
Rob Prins, engineering professor, James Madison University.
Irfan Ahmed, assistant professor, computer science, Virginia Commonwealth University
Smart manufacturing includes interpretation of digitally communicated designs into code intended for execution on connected devices such as those associated with additive manufacturing. Simulated cyberattacks on 3-D printers have demonstrated that mechanical properties of a part can be maliciously modified without causing defects that are apparent to the typical inspection process. Such concealed defects are of concern because they may result in parts failing after they are integrated into a larger system and deployed. Failure in this mode jeopardizes the entire system. Researchers have investigated ways to detect cyberattacks on manufacturing code (G-code) including methods that predict expected 3-D printer behavior based on known good code and compare these expectations to observed reality during the build process. The proposed research includes two thrusts. The first thrust will focus on development of several cyberattack simulations applicable to 3-D printers (including generation of internal voids and manipulation of process temperatures). The effect of these different attacks on mechanical properties will be observed through testing. The second thrust will focus on G-code modeling to predict printer behavior; G-code models will be compared to actual printer behavior using an integrity-checking framework. Actual printer behavior will be monitored via a novel technique that is more direct than methods currently described in the literature.