DETECTING PART ANOMALIES INDUCED BY CYBER ATTACKS ON A POWDER BED FUSION ADDITIVE MANUFACTURING SYSTEM

Elizabeth Kurkowski, Mason Rice, Sujeet Shenoi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Additive manufacturing systems are highly vulnerable to cyber attacks that sabotage parts and print environments during the designing, slicing and printing steps of the process chains. Due to the complex cyber-physical nature of additive manufacturing systems, cyber attacks are difficult to detect and mitigate, and impossible to eliminate entirely. Therefore, it is imperative to develop rapid and reliable non-destructive testing methods for detecting anomalies in printed parts. This chapter describes a novel anomaly detection method developed for a selective laser sintering type of powder bed fusion system. The method does not engage computing-intensive machine learning to detect anomalies, relying instead on three side channels, print bed movement, laser firing time and print chamber temperature, that underlie the physics of selective laser sintering. The side channels provide adequate detection coverage while reducing the sensor requirements; they are also robust to noise, which enhances the detection of printed part anomalies. Experimental results demonstrate the efficacy of the anomaly detection method under attacks that target the mechanical properties of printed parts. The cost of the sensors and peripheral devices is minimal and anomaly detection for each test part requires less than three seconds.

Original languageEnglish
Title of host publicationCritical Infrastructure Protection XVI - 16th IFIP WG 11.10 International Conference, ICCIP 2022, Revised Selected Papers
EditorsJason Staggs, Sujeet Shenoi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages175-203
Number of pages29
ISBN (Print)9783031201363
DOIs
StatePublished - 2022
Event16th Annual IFIP WG 11.10 International Conference on Critical Infrastructure Protection, ICCIP 2022 - Virtual, Online
Duration: Mar 14 2022Mar 15 2022

Publication series

NameIFIP Advances in Information and Communication Technology
Volume666 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference16th Annual IFIP WG 11.10 International Conference on Critical Infrastructure Protection, ICCIP 2022
CityVirtual, Online
Period03/14/2203/15/22

Funding

This research was supported by the National Science Foundation under Grant no. DGE 1501177 and by UT-Battelle under Contract no. DE-AC05-00OR22725 with the U.S. Department of Energy.

Keywords

  • Additive manufacturing
  • anomaly detection
  • powder bed fusion

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