Application of Microfracture Analysis to Fatigue Fractures in Materials through Non-Destructive Tests

• Published in: Materials Vol. 17; no. 4; p. 772
• Main Authors: Sánchez-Santana, Ulises, Presbítero-Espinosa, Gerardo, Quiroga-Arias, José María
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2024
• Subjects: Artificial neural networks; Bones; Composite materials; Computed tomography; CT imaging; Deep learning; Equipment and supplies; Failure; Fatigue failure; Fatigue tests; Forecasts and trends; Fracture mechanics; Fractures; Image processing; Image resolution; Machine learning; Microfracture; neural network; Neural networks; Nondestructive testing; Pixels; Quality control; simulation analyses; Software; Stress fractures; Tomography; image processing; microfracture; simulation analyses; neural network
• DOI: 10.3390/ma17040772
• ISSN: 1996-1944

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 fractures 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). In this investigation, mechanical fatigue tests were carried out on specimens smaller than 4 mm , made of different industrial materials. Their subsequent analysis was performed through precision computed tomography, in search for 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 resolutions of 480 × 854 or 960 × 960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those without are classified using the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images containing this feature are interpreted using image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and microfracture-type metrics. All values are obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution. The growth of microfractures will be used to define trends in the development of fatigue fractures through the studies presented.