Infill defective detection system augmented by semi-supervised learning

Jinwoo Song, Young B. Moon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In an effort to identify cyber-attacks on infill structures, detection systems based on supervised learning have been attempted in Additive Manufacturing (AM) security investigations. However, supervised learning requires a myriad of training data sets to achieve acceptable detection accuracy. Besides, since it is impossible to train for unprecedented defective types, the detection systems cannot guarantee robustness against unforeseen attacks. To overcome such disadvantages of supervised learning, This paper presents infill defective detection system (IDDS) augmented by semi-supervised learning. Semi-supervised learning allows classifying a sheer volume of unlabeled data sets by training a comparably small number of labeled data sets. Additionally, IDDS exploits self-training to increase the robustness against various defective types that are not pre-trained. IDDS consists of the feature extraction, pre-training, self-training. To validate the usefulness of IDDS, five defective types were designed and tested with IDDS, which was trained by only normal labeled data sets. The results are compared with the basis accuracy from the perceptron network model with supervised learning.

Original languageEnglish (US)
Title of host publicationAdvanced Manufacturing
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791884492
DOIs
StatePublished - 2020
EventASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020 - Virtual, Online
Duration: Nov 16 2020Nov 19 2020

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume2B-2020

Conference

ConferenceASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
CityVirtual, Online
Period11/16/2011/19/20

Keywords

  • Additive manufacturing
  • Cyber-Physical attack
  • Detection system
  • Layered image
  • Machine learning
  • Neural network
  • Semi-supervised learning

ASJC Scopus subject areas

  • Mechanical Engineering

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