Abstract
AbstractAdditive 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. In this paper, we present an online detection mechanism for the malicious attempts on AM systems, which taps into both audio and video signals collected during the printing process. For audio signals, we propose to monitor the shift of patterns in the spectrogram and dominant frequencies via a control chart designed based on the Wasserstein metric. For video signals, we propose to monitor the change in the reconstructed path of the extruder via a Hausdorff metric. We then show the effectiveness of our methods in a case study using an Ender 3D printer, where the cyber‐incidence of altering the internal fill density can be easily identified in an online manner.
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