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    <title language="eng">Application of the thermal shock response spectrum (TSRS) methodology to various forms of heat sources by pulse thermography and comparison by using a rotating line scan contour search algorithm</title>
    <abstract language="eng">In this paper, we propose a novel contour search algorithm (CSA) for image processing. Its efficacy is evaluated through a comparative analysis with established techniques such as Canny Edge and Snakes: Active contour models, utilizing infrared thermography (IRT) images. Based on the new CSA, we investigate the influence of different pulse shapes on the IRT post-processing methodology, particularly focusing on the thermal shock response spectrum (TSRS), using two different heat sources: xenon flash lamps and a laser. Moreover, this allows for a more precise quantitative comparison of the TSRS with existing IRT post-processing techniques, including pulse phase thermography and thermal signal reconstruction, concerning the detection of defects in composite materials, particularly in carbon fiber-reinforced polymer. A quantitative comparison was performed using the Tanimoto criterion and signal-to-noise ratio. A more detailed analysis is conducted to identify inherent limitations and potential benefits of the new TSRS methodology. We further investigate and experimentally confirm our previous finding on the qualitative correlation between the one-dimensional thermal N-layer model and test data from the TSRS optimization process for defect determination. This correlation can eliminate the time-consuming optimization step, making TSRS a more attractive alternative to common IRT methods and enhancing the quantitative description of defects.</abstract>
    <parentTitle language="eng">Journal of Applied Physics</parentTitle>
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    <author>H. Yagdjian</author>
    <author>Julien Lecompagnon</author>
    <author>Philipp Daniel Hirsch</author>
    <author>Mathias Ziegler</author>
    <author>M. Gurka</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-destructive testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NDT</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Defect identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Contour search</value>
    </subject>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
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    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="themenfelder" number="">Leichtbau</collection>
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    <title language="eng">Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model</title>
    <abstract language="eng">In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model. The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data postprocessing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.</abstract>
    <parentTitle language="eng">Infrared Physics &amp; Technology</parentTitle>
    <identifier type="issn">1350-4495</identifier>
    <identifier type="doi">10.1016/j.infrared.2024.105582</identifier>
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