Each week, we publish layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
The article featured today is from Quality and Reliability Engineering International, with the full article now available to read here.
Online detection of cyber-incidents in additive manufacturing systems via analyzing multimedia signals. Qual Reliab Eng Int. 2021; 1– 17. https://doi.org/10.1002/qre.2953, , , et al.
Additive Manufacturing (AM) or 3D printing is an emerging manufacturing technology that plays a growing role in both industrial and consumer settings. However, security concerns of AM systems have been raised among researchers and practitioners. In this paper, an online detection mechanism for the malicious attempts on AM systems, which taps into both audio and video signals collected during the printing process is proposed. For audio signals, a control chart together with a distance metric is used to detect shift in pattern of the audio signal in the frequency domain. For video signals, the reconstruction of the movement path of the printing extruder is performed through a simple yet effective algorithm. Similar to using audio signals, a control chart together with a distance metric is utilized to detect the divergence between the reconstructed path and the path from the original design file. The advantages and disadvantages of the two different methods are discussed based on their cost and robustness. The effectiveness of the proposed methods is examined in a case study using an Ender 3D printer, where a cyber-incidence targets at altering the internal fill density. The results demonstrate a much quicker and more robust detection of the cyber-incidence under various levels of modified internal fill density using the proposed methods when compared against other benchmarks.