Lamb Wave Propagation-based Damage Identification for Quasi-isotropic CF/EP Composite Laminates Using Artificial Neural Algorithm: Part II - Implementation and Validation

Zhongqing Su, Lin Ye

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)

Abstract

Active transducer networks using distributed piezoelectric actuator/sensor were designed in terms of a concept of ‘Standard Sensor Unit’ (SSU). Functionally integrating the artificial neural networks well-trained by Damage Parameters Database (DPD) developed in Part I, an active online structural health monitoring (SHM) system was configured on a VXI platform, which was then validated by quantitatively identifying hole-type defects in quasi-isotropic [0/45/-45/90]s CF/EP (T650/F584) composite laminates. The system has exhibited excellent ability to quantitatively assess the damaged parameters, including presence, location, geometric identity, and orientation. Additionally, the reliability and performance of the SHM system on the inherent network configurations, such as architecture, training pattern, training function, and distribution of transducers, were also evaluated.
Original languageEnglish
Pages (from-to)113-125
Number of pages13
JournalJournal of Intelligent Material Systems and Structures
Volume16
Issue number2
DOIs
Publication statusPublished - 1 Jan 2005
Externally publishedYes

Keywords

  • artificial neural network
  • composite laminates
  • damage identification
  • structural health monitoring
  • transducer network

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanical Engineering

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