Impact damage detection in carbon fibre composites using HTS SQUIDs and neural networks

D. Graham, P. Maas, G.B. Donaldson, C. Carr

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A neural network-based data analysis tool, developed to speed the damage detection process for the NDE of impact damaged carbon fibre composites, is discussed. A feature extraction method utilising a gradient threshold search function and a feed forward neural network for pattern recognition were used to develop the system. Impact damaged carbon composite sample plates were scanned with an eddy current-based NDE setup using HTS SQUID gradiometers and double-D excitation coils. Detection of damage sites in data affected by noise spikes caused by environmental disturbances is demonstrated. Finally, a possible design for a future entirely automated scanning system is also introduced.
Original languageEnglish
Pages (from-to)565-570
Number of pages5
JournalNDT and E International
Volume37
Issue number7
DOIs
Publication statusPublished - Oct 2004

Keywords

  • eddy current
  • neural network
  • composite laminates

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