Design and Demonstration of Automated Data Analysis Algorithms for Ultrasonic Inspection of Complex Composite Panels with Bonds

Abstract

To address the data review burden and improve the reliability of the ultrasonic inspection of large composite structures, automated data analysis (ADA) algorithms have been developed to make calls on indications that satisfy the detection criteria and minimize false calls. The original design followed standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. However, certain complex panels with varying shape, ply drops and the presence of bonds can complicate this interpretation process. In this paper, enhancements to the automated data analysis algorithms are introduced to address these challenges. To estimate the thickness of the part and presence of bonds without prior information, an algorithm tracks potential backwall or bond-line signals, and evaluates a combination of spatial, amplitude, and time-of-flight metrics to identify bonded sections. Once part boundaries, thickness transitions and bonded regions are identified, feature extraction algorithms are applied to multiple sets of through-thickness and backwall C-scan images.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2016
Accession Number
AD1036014

Entities

People

  • David .s. Forsyth
  • John C. Aldrin
  • John T. Welter

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Amplitude
  • Composite Materials
  • Composite Structures
  • Data Analysis
  • Demonstrations
  • Extraction
  • Feature Extraction
  • Inspection
  • Materials
  • Standards
  • Test And Evaluation
  • Test Sets
  • Thickness
  • Ultrasonic Inspection

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Structural Health Monitoring of Composite Structures.

Technology Areas

  • AI & ML