UNSUPERVISED MACHINE LEARNING ALGORITHMS FOR ANALYSIS OF LOW VELOCITY IMPACT DAMAGE IN COMPOSITE STRUCTURES FROM CT IMAGE DATA

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

Abstract

In this work, novel unsupervised machine learning (ML) algorithms for automatic image segmentation for the analysis of the micro-CT data for impact damage assessment in the composite materials have been developed. The algorithms are based on the statistical distances including the Kullback-Leibler divergence, the Helling distance, and the Renyi divergence. The developed algorithms have been applied to the analysis of low velocity impact damage in carbon fiber reinforced polymer (CFRP) composites. The grayscale images from the CT scans of the impacted CFRP specimens have been analyzed to identify and isolate impact damage and optimal statistics-based damage thresholds have been found. The results show that the developed algorithms enable not only an automatic image segmentation, but also deliver statistics-based rigorous damage thresholds.

Original languageEnglish (US)
Title of host publicationAdvanced Materials
Subtitle of host publicationDesign, Processing, Characterization and Applications; Advances in Aerospace Technology
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791886656
DOIs
StatePublished - 2022
Externally publishedYes
EventASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022 - Columbus, United States
Duration: Oct 30 2022Nov 3 2022

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume3

Conference

ConferenceASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022
Country/TerritoryUnited States
CityColumbus
Period10/30/2211/3/22

Keywords

  • composite materials
  • computed tomography
  • image segmentation
  • impact damage

ASJC Scopus subject areas

  • Mechanical Engineering

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