Detecting attacks in cybermanufacturing systems: Additive manufacturing example

Mingtao Wu, Heguang Zhou, Longwang Lucas Lin, Bruno Silva, Zhengyi Song, Jackie Cheung, Young Bai Moon

Research output: Contribution to journalArticle

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Abstract

CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. However, realizing the vision requires that its security be adequately ensured. This paper presents a vision-based system to detect intentional attacks on additive manufacturing processes, utilizing machine learning techniques. Particularly, additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. In order to detect such infill defects, the research uses simulated 3D printing process images as well as actual 3D printing process images to compare accuracies of machine learning algorithms in classifying, clustering and detecting anomalies on different types of infills. Three algorithms-(i) random forest, (ii) k nearest neighbor, and (iii) anomaly detection-have been adopted in the research and shown to be effective in detecting such defects.

Original languageEnglish (US)
Article number06005
JournalMATEC Web of Conferences
Volume108
DOIs
StatePublished - May 31 2017

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ASJC Scopus subject areas

  • Chemistry(all)
  • Engineering(all)
  • Materials Science(all)

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