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Algorithm for Fatigue Crack Initiation Assessment Based on Industrial Photogrammetry

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  • معلومة اضافية
    • بيانات النشر:
      MDPI AG, 2024.
    • الموضوع:
      2024
    • Collection:
      LCC:Technology
      LCC:Engineering (General). Civil engineering (General)
      LCC:Biology (General)
      LCC:Physics
      LCC:Chemistry
    • نبذة مختصرة :
      Industrial photogrammetry is a reliable method to achieve submillimeter accuracy when mapping 2D or 3D objects. In the field of fatigue testing of steel welded details, it can be used to find a new method of crack initiation assessment. Fatigue testing is an important method for determining and predicting the durability of structural details in service. The research presented in this paper is based on a computer vision algorithm developed using the open-source code OpenCV library and the Oriented FAST and Rotated BRIEF (ORB) method to provide a solution for the assessment of crack initiation. Within this research, a method for determining the crack initiation period using polynomial functions of a certain degree is developed. The developed algorithm fully automatically determines the test specimen displacement for all imagery and assesses the crack initialization period by polynomial interpolation with a percentage threshold. The algorithm shows us the best results based on a 26th-degree polynomial with a deviation from the critical value of 5%. The validation of the algorithm was carried out using completely independently recorded data from the hydraulic press used for fatigue tests. The results of all test specimens show that the percentage accuracy of determination crack initiation period is between −0.04% for test specimens S355-TA-AW-02 and S355-TA-HFMI-03 and −0.82% for test specimen S355-TA-HFMI-03, with the mean of all results being 0.39%.
    • File Description:
      electronic resource
    • ISSN:
      2076-3417
    • Relation:
      https://www.mdpi.com/2076-3417/14/15/6501; https://doaj.org/toc/2076-3417
    • الرقم المعرف:
      10.3390/app14156501
    • الرقم المعرف:
      edsdoj.b8b664a062c1436eafe152aebcfbb410