Digital Audio Signature for 3D Printing Integrity

Abstract

Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer's stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes, and different audio recorder positions.

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

“…The defective part came into contact with the nozzle, which resulted in altered acoustic emissions. Moreover, recording devices can be placed next to the 3D printer [207][208][209][210]. Using this setup, Chhetri et al [207] reconstructed the geometry of layers based on the acoustic emissions of the axes and motors.…”

Detecting Sabotage Attacks in Additive Manufacturing Using Actuator Power Signatures

“…The authors used this to detect deviations in the printed object's geometry, achieving a 77.45% precision in deviation detection. Belikovetsky et al [24] also used acoustic emanations to detect deviations in a manufacturing process. The authors applied principal component analysis (PCA) to generate an acoustic signature of a benign 3D printing process; the validity of follow-up printing processes can be verified by comparing the acoustic signatures.…”

“…However, while visual comparison provides an intuitive way to illustrate our idea, it is not practical when considering traces that are hundreds or thousands of times longer than the given example. To enable an automated comparison, we built upon an approach proposed by Belikovetsky et al [24] for acoustic side-channel emanations. We also introduce several significant modifications to this approach.…”

“…For the evaluation of the proposed solution, we follow an approach proposed by Belikovetsky et al [24] -to test the approach on and identify the detectability threshold for the minimal possible manipulation primitives, i.e., individual G-code commands. Belikovetsky et al refer to these as atomic modifications, and proposed the following categories of tests: (a) insertion of a new G-code command; (b) deletion of a G-code command present in the original STL file; (c) a reordering of two G-code commands present in the original STL file; (d) replacement of a printing move command with a non-printing move command (''smart void'').…”

Additive Manufacturing Cyber-Physical System: Supply Chain Cybersecurity and Risks

“…Thorough survey on risk taxonomy for additive manufacturing is presented in previous published works [16], [17]. Developments have taken place in mitigating risks of counterfeiting [18], [19], transparency [20], [21] and the manufacturing process [22], [23] in the AM security. Nonetheless, the extant body of literature of SCRM for AM remains preliminary and conceptual [24].…”