Abstract

Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low-energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient structural health monitoring methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this article, a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical feature matrices, composed of features extracted from the time and frequency domains, are developed. Kernel principal component analysis is used to compress and classify the statistical feature matrices. Compared with traditional principal component analysis algorithm, kernel principal component analysis method shows better feature clustering and damage quantification capabilities. A new damage index, formulated using the Mahalanobis distance, is defined to quantify impact damage. The developed methodology has been validated using low-velocity impact experiments with a sandwich composite wing.

Original languageEnglish (US)
Pages (from-to)2074-2083
Number of pages10
JournalJournal of Intelligent Material Systems and Structures
Volume24
Issue number17
DOIs
StatePublished - Nov 2013

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Principal component analysis
Composite structures
Monitoring
Composite materials
Naval vessels
Damage detection
Structural health monitoring
Aircraft
Degradation
Defects
Experiments

Keywords

  • active sensing
  • damage index
  • damage quantification
  • kernel principal component analysis
  • Low-velocity impact damage
  • macro fiber composite
  • sandwich composite wing
  • structural health monitoring

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanical Engineering

Cite this

Low-velocity impact damage monitoring of a sandwich composite wing. / Liu, Yingtao; Chattopadhyay, Aditi.

In: Journal of Intelligent Material Systems and Structures, Vol. 24, No. 17, 11.2013, p. 2074-2083.

Research output: Contribution to journalArticle

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