Case Study of Model-Based Inversion of the Angle Beam Ultrasonic Response from Composite Impact Damage (Postprint)

Abstract

The U.S. Air Force seeks to improve lifecycle management of composite structures. Nondestructive characterization of damage is a key input to this framework. One approach to characterization is model-based inversion of ultrasound inspection data; however, the computational expense of simulating the response from damage represents a major hurdle for practicality. A surrogate forward model with greater computational efficiency and sufficient accuracy is, therefore, critical to enable damage characterization via model-based inversion. In this work, a surrogate model based on Gaussian process regression (GPR) is developed on the chirplet decomposition of the simulated quasi-shear scatter from delamination-like features that form a shadowed region within a representative composite layup. The surrogate model is called in the solution of the inverse problem for the position of the hidden delamination, which is achieved with <0.5% error in <20min on a workstation computer for two unique test cases. These results demonstrate that solving the inverse problem from the ultrasonic response is tractable for composite impact damage with hidden delaminations.

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Document Details

Document Type
Technical Report
Publication Date
Jun 05, 2018
Accession Number
AD1042113

Entities

People

  • Daniel Sparkman
  • John Aldrin
  • John Welter
  • John Wertz
  • Laura Homa

Organizations

  • University of Dayton Research Institute

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Case Studies
  • Composite Materials
  • Delamination
  • Engineering
  • Errors
  • Gaussian Processes
  • Inspection
  • Inverse Problems
  • Materials
  • Supervised Machine Learning
  • Two Dimensional
  • Ultrasounds
  • Waves
  • X-Ray Computed Tomography

Readers

  • Computational Modeling and Simulation
  • Structural Health Monitoring of Composite Structures.
  • Wave Propagation and Nonlinear Chaotic Dynamics.