Cyber-physical systems have been widely adopted in a number of industries, including healthcare, critical infrastructure, and manufacturing. The connectivity of machines, which characterizes cyber-physical systems and provides many of their benefits, leaves them vulnerable to cyber-attacks. Within manufacturing, cyber-attacks can compromise end-use parts, resulting in catastrophic part failure. Researchers have recently started to investigate the vulnerabilities within connected additive manufacturing machines, demonstrating vulnerabilities and proposing mitigation strategies. While informative, findings from past work are limited by the physical machines used in experiments, making results difficult to generalize to other systems. Further, physical experiments take time, waste stock material, and may not be generalizable for other machines. The goal of this work is to provide a digital testbed for future research to characterize the vulnerabilities of manufacturing CPS and develop new methods to effectively identify compromised parts. This paper proposes and validates an inexpensive and accessible simulation to accurately predict the physical output of a fused deposition modeling additive manufacturing machine. This simulation allows for efficient testing of attack vectors and determine the probability of attack detection using quality control methods. This study lays the groundwork for future research exploring the vulnerabilities in engineering design processes as manufacturers become increasingly reliant on connected production equipment.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Materials Science(all)
- Engineering (miscellaneous)
- Industrial and Manufacturing Engineering