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<p>Conventional methods for predicting crack growth in composite materials often face limitations due to the complex nature of these materials, resulting in inaccurate predictions of crack initiation and propagation. To overcome these limitations, the research employs an advanced image processing technique to capture the behavior of GLASS/PA6 thermoplastic composites. Three algorithms (Euclidean distance, correlation tracking, and corner tracking) are utilized to predict strain and displacement fields. The methodology involves recording displacement values through a sequence of images and applying image processing techniques to identify distinct surface features on the material. The results demonstrate strong consistency between numerical simulations and experimental data, indicating the effectiveness of this approach.</p>

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Mixed mode (I/II) fracture of Glass/PA6 thermoplastic composites based on hybrid distance and correlation-based multiple target tracking methods

  • 期刊论文

  • 发表日期：

    2025年6月25日

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  • Seyed Aref Mousavi Teroujeni,
  • Nabi Mehri Khansari,
  • Hamed Danandeh Hesar

摘要

Conventional methods for predicting crack growth in composite materials often face limitations due to the complex nature of these materials, resulting in inaccurate predictions of crack initiation and propagation. To overcome these limitations, the research employs an advanced image processing technique to capture the behavior of GLASS/PA6 thermoplastic composites. Three algorithms (Euclidean distance, correlation tracking, and corner tracking) are utilized to predict strain and displacement fields. The methodology involves recording displacement values through a sequence of images and applying image processing techniques to identify distinct surface features on the material. The results demonstrate strong consistency between numerical simulations and experimental data, indicating the effectiveness of this approach.