Automatic image segmentation of CT data from the low velocity impact tests of CFRP composites

Olesya I. Zhupanska, Pavlo A. Krokhmal

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

Abstract

In this work, the role of image segmentation in the analysis of the micro-CT data for the low velocity damage assessment in carbon fiber reinforced polymer (CFRP) composites is discussed. A novel automatic image segmentation method based on the unsupervised learning approach and the Kullback–Leibler divergence is presented. The method has been successfully applied to identify and isolate impact damage in the CFRP composites subjected to the low velocity impact. The results show that the method enables not only an automatic image segmentation, but also delivers a statistics based rigorous damage threshold.

Original languageEnglish (US)
Title of host publication36th Technical Conference of the American Society for Composites 2021
Subtitle of host publicationComposites Ingenuity Taking on Challenges in Environment-Energy-Economy, ASC 2021
EditorsOzden Ochoa
PublisherDEStech Publications
Pages721-728
Number of pages8
ISBN (Electronic)9781713837596
StatePublished - 2021
Event36th Technical Conference of the American Society for Composites 2021: Composites Ingenuity Taking on Challenges in Environment-Energy-Economy, ASC 2021 - College Station, Virtual, United States
Duration: Sep 20 2021Sep 22 2021

Publication series

Name36th Technical Conference of the American Society for Composites 2021: Composites Ingenuity Taking on Challenges in Environment-Energy-Economy, ASC 2021
Volume2

Conference

Conference36th Technical Conference of the American Society for Composites 2021: Composites Ingenuity Taking on Challenges in Environment-Energy-Economy, ASC 2021
Country/TerritoryUnited States
CityCollege Station, Virtual
Period9/20/219/22/21

ASJC Scopus subject areas

  • Ceramics and Composites

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