Damage assessment of laminated composites using unsupervised autonomous features

Asif Khan, Heung Soo Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms.

Original languageEnglish
Pages (from-to)2123-2148
Number of pages26
JournalJournal of Thermoplastic Composite Materials
Volume37
Issue number6
DOIs
StatePublished - Jun 2024

Keywords

  • Laminated composites
  • autonomous features
  • damage assessment
  • lamb waves
  • sparse autoencoder

Fingerprint

Dive into the research topics of 'Damage assessment of laminated composites using unsupervised autonomous features'. Together they form a unique fingerprint.

Cite this