Authors: Muhammad Haris Rais (Virginia Commonwealth University), Ye Li (Bradley University), and Irfan Ahmed (Virginia Commonwealth University)
DFRWS USA 2020
Increasingly, 3D printers are used to manufacture functional components of commercial products, such as engine and turbine parts and cabin interior of an airplane. In case of a catastrophic incident, these parts may be subject to a forensic investigation. In this presentation, we propose a forensic readiness framework for a 3D printer and discuss its implementation on a FDM (fused deposition modeling)-based 3D printer, Ultimaker 3. The framework deploys out-of-bound sensors on a printer to log the critical parameters of the machine behavior during the printing of a 3D object. It consists of a set of algorithms to analyze sensor data in the time and space domain to answer several questions about an investigation such as: What was the shape, orientation, toolpath sequence of the 3D printed object? Which infill pattern was used? What was the layer thickness of each layer? What was the temperature profile for the nozzle and the printing bed? For the proof-of-concept, we first demonstrate thermodynamic and kinetic attacks that damage a 3D printing object without creating substantial visual deformation on an object by making slight changes to the manufacturing parameters of a 3D printing process such as nozzle temperature and positioning. We deploy ubiquitous, easy to mount, and inexpensive sensors on Ultimaker 3 printer to evaluate the effectiveness of the framework in terms of forensic readiness and detection of any suspicious printed objects.