Sánchez-Santana, U.; Presbítero-Espinosa, G.; Quiroga-Arias, J.M. Application of Microfracture Analysis to Fatigue Fractures in Materials through Non-Destructive Tests. Materials2024, 17, 772.
Sánchez-Santana, U.; Presbítero-Espinosa, G.; Quiroga-Arias, J.M. Application of Microfracture Analysis to Fatigue Fractures in Materials through Non-Destructive Tests. Materials 2024, 17, 772.
Sánchez-Santana, U.; Presbítero-Espinosa, G.; Quiroga-Arias, J.M. Application of Microfracture Analysis to Fatigue Fractures in Materials through Non-Destructive Tests. Materials2024, 17, 772.
Sánchez-Santana, U.; Presbítero-Espinosa, G.; Quiroga-Arias, J.M. Application of Microfracture Analysis to Fatigue Fractures in Materials through Non-Destructive Tests. Materials 2024, 17, 772.
Abstract
Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fracture, according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). This investigation was carried out mechanical fatigue tests on specimens smaller than 4 mm2 in section of different industrial materials for their subsequent analysis through precision computed tomography in search of microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with 480x854 or 960x960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those that do not are classified by the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images that do contain this feature are interpreted by image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and type microfracture metrics. All values will be obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
