Structured graph framework improves security analysis of 3D printing meshes
SAMS: A Structured Graph Framework for Securing Additive Manufacturing Systems
Graphics
Summary
3D printing creates objects layer by layer, but the digital designs can have hidden flaws caused by errors or tampering. These flaws may weaken the final product without being easy to find. The authors propose SAMS, a way to represent 3D designs as layered graphs that capture detailed shape information. This structure helps spot vulnerabilities more effectively and simplifies the analysis of complex designs.
What this means in practice
- •For manufacturing engineers: Analyze 3D print designs for hidden flaws to prevent weaknesses caused by tampering or errors.
- •For robotics developers: Enhance the reliability of 3D printed robotic parts by detecting geometry issues before fabrication.
Authors
Sarah Roscoe, Lachlan Hovany, Kamila Maciocha, Witawas Srisa-an, Jitender S. Deogun
Abstract
Additive manufacturing, also known as 3D printing, is significant for its use in industries such as aerospace, robotics, medicine, and construction. However, digital mesh files remain vulnerable to geometry defects which can be introduced through malicious tampering or designer error. This can lead to hampered structural integrity and impede its use due to unexpected degradation of the part. To address these challenges, we propose SAMS, a multi-layered hierarchical graph framework designed to analyze meshes to enhance the Security of Additive Manufacturing Systems. SAMS captures geometric and topological information across mesh, facet, and surface levels, providing an adaptable architecture for targeted mesh processing tasks. We evaluate SAMS's accuracy against existing mesh processing tools. The graph construction results in rich structural representation and has the potential to significantly reduce downstream analytical complexity when detecting vulnerabilities. This paper presents SAMS, its formal graph-theoretic properties, and releases an open-source Python 3.12 implementation on GitHub (https://anonymous.4open.science/r/SAMS-graph-classes/) to facilitate future research in secure additive manufacturing systems software.