@article{01HGFXXX2MTQ9JGFC7020QGBGK,
  abstract     = {{Matching infrared thermography (IRT) with deep learning-based computer vision has recently gained a lot of interest for automated defect assessment in materials. One of the remaining bottlenecks concerns the necessity of a large and diverse experimental and/or virtual training dataset in order to achieve a sufficiently generalizable computer vision algorithm. This paper presents a parametrized 3D finite element (FE) framework, implemented in Fortran90, for efficiently simulating optical infrared thermographic inspection of multi-layer anisotropic media and establishing large-scale virtual dataset with sufficient diversity. The interface element is introduced for the modelling of an imperfect thermal contact, allowing to simulate a variety of defect types. The flexibility of the interface element makes it possible to simulate delaminations with different thickness using the same dis-cretized model. Validation is done for two benchmark cases which are representative for a fiber reinforced polymer laminate with delamination-like defects. In order to achieve true-to-nature thermographic simulation data, non-uniform heating conditions are adopted from experiment, and a stochastic morphology generator is introduced for modelling realistic irregular defect geometries. To demonstrate the added value of a large, diverse and true-to-nature virtual database for computer vision applications, a Faster-RCNN model was trained on a generated virtual dataset for the detection of delamination-like defects in fiber reinforced polymer laminates. Application of the trained Faster-RCNN on experimental thermographic data yields excellent inference results, illustrating the high generalization ability of the virtually trained object detector.}},
  articleno    = {{102787}},
  author       = {{Tong, Zongfei and Hedayatrasa, Saeid and Cheng, Liangliang and  Pei, Cuixiang and  Chen, Zhenmao and  Xie, Shejuan and Kersemans, Mathias}},
  issn         = {{0963-8695}},
  journal      = {{NDT & E INTERNATIONAL}},
  keywords     = {{Infrared thermography,Fiber reinforced polymer,Finite element method,Interface element,Computer vision,Virtual dataset,IDENTIFICATION,ENHANCEMENT,DAMAGE,FORM}},
  language     = {{eng}},
  pages        = {{16}},
  title        = {{An efficient parametrized optical infrared thermography 3D finite element framework for computer vision applications}},
  url          = {{http://doi.org/10.1016/j.ndteint.2023.102787}},
  volume       = {{135}},
  year         = {{2023}},
}

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