A novel Bayesian imaging method for probabilistic delamination detection of composite materials

Tishun Peng, Abhinav Saxena, Kai Goebel, Yibing Xiang, Shankar Sankararaman, Yongming Liu

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

A probabilistic framework for location and size determination for delamination in carbon-carbon composites is proposed in this paper. A probability image of delaminated area using Lamb wave-based damage detection features is constructed with the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using the proposed Bayesian imaging method (BIM) is presented. Next, a fatigue testing setup for carbon-carbon composite coupons is described. The Lamb wave-based diagnostic signal is then interpreted and processed. Next, the obtained signal features are incorporated in the Bayesian imaging method for delamination size and location detection, as well as the corresponding uncertainty bounds prediction. The damage detection results using the proposed methodology are compared with x-ray images for verification and validation. Finally, some conclusions are drawn and suggestions made for future works based on the study presented in this paper.

Original languageEnglish (US)
Article number125019
JournalSmart Materials and Structures
Volume22
Issue number12
DOIs
StatePublished - Dec 2013

ASJC Scopus subject areas

  • Signal Processing
  • Civil and Structural Engineering
  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Electrical and Electronic Engineering

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