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Modeling of microscope images for early detection of fatigue cracks in structural materials

  • Najah F. Ghalyan
  • Ibrahim F. Ghalyan
  • Asok RayEmail author
ORIGINAL ARTICLE
  • 45 Downloads

Abstract

From the perspectives of health monitoring and life extension of structural materials, this paper addresses the problem of early detection of fatigue cracks in metallic materials (e.g., polycrystalline alloys). To this end, optical images have been collected from an ensemble of test specimens to construct computationally efficient models of crack evolution; these images are segmented into two major categories. The first category comprises images of (structurally) healthy specimens, while the second category contains images of specimens with cracks, including those in early stages of crack evolution. Based on this information, algorithms for early detection of crack formation are formulated in the setting of image classification, where the bag-of-words (BoW) technique has been used to develop models of the sensed images from a microscope, resulting in computationally efficient crack detection algorithms. To evaluate the performance of these crack detection algorithms, experiments have been conducted on a special-purpose fatigue testing apparatus, equipped with a computer-controlled and computer-instrumented confocal microscope system. The results of experimentation with multiple test specimens show excellent crack detection capabilities when the proposed BoW-based feature extraction is combined with quadratic support vector machine (QSVM) for pattern classification. Comparative evaluation with other classification tools establishes superiority of the proposed BoW/QSVM technique.

Keywords

Bag-of-words Crack detection Image classification 

Notes

Funding information

The work reported in this paper has been supported in part by the U.S. Air Force Office of Scientific Research (AFOSR) under Grant Nos. FA9550-15-1-0400 and FA9550-18-1-0135 in the area of dynamic data-driven application systems (DDDAS). Any opinions, findings, and conclusions in this paper are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Najah F. Ghalyan
    • 1
  • Ibrahim F. Ghalyan
    • 2
  • Asok Ray
    • 3
    Email author
  1. 1.Department of Mechanical EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of Mechanical and Aerospace Engineering, Tandon School of EngineeringNew York UniversityBrooklynUSA
  3. 3.Department of Mechanical Engineering and Department of MathematicsThe Pennsylvania State UniversityUniversity ParkUSA

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