{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:45:36Z","timestamp":1764175536104,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Consejo Nacional de Humanidades Ciencia y Tecnolog\u00eda (CONAHCYT)","award":["FORDECYT-PRONACES\/425880\/2020"],"award-info":[{"award-number":["FORDECYT-PRONACES\/425880\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>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 mm2, 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 \u00d7 854 or 960 \u00d7 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 (\u03bcm) 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.<\/jats:p>","DOI":"10.3390\/ma17040772","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T07:50:12Z","timestamp":1707205812000},"page":"772","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of Microfracture Analysis to Fatigue Fractures in Materials through Non-Destructive Tests"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4403-5574","authenticated-orcid":false,"given":"Ulises","family":"S\u00e1nchez-Santana","sequence":"first","affiliation":[{"name":"Centro de Ingenier\u00eda y Desarrollo Industrial, Pie de la Cuesta 702, Desarrollo San Pablo, Quer\u00e9taro 76130, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerardo","family":"Presb\u00edtero-Espinosa","sequence":"additional","affiliation":[{"name":"Centro de Ingenier\u00eda y Desarrollo Industrial, Pie de la Cuesta 702, Desarrollo San Pablo, Quer\u00e9taro 76130, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Mar\u00eda","family":"Quiroga-Arias","sequence":"additional","affiliation":[{"name":"Universidad Aeron\u00e1utica en Quer\u00e9taro, Quer\u00e9taro 76278, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"ref_1","unstructured":"Guten, G., and Shorter, F. (1997). Running Injuries, W.B. Saunders Company."},{"key":"ref_2","first-page":"38","article-title":"Numerical analysis of stress intensity factor in specimens with different fillet geometry subjected to bending","volume":"12","author":"Lewandowski","year":"2018","journal-title":"Acta Mech. Autom."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103605","DOI":"10.1016\/j.autcon.2021.103605","article-title":"Crack segmentation through deep convolutional neural networks and heterogeneous image fusion","volume":"125","author":"Shanglian","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"133582","DOI":"10.1016\/j.conbuildmat.2023.133582","article-title":"Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network","volume":"408","author":"Domen","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107823","DOI":"10.1016\/j.engfracmech.2021.107823","article-title":"Neural network segmentation methods for fatigue crack images obtained with X-ray tomography","volume":"252","author":"Ce","year":"2021","journal-title":"Eng. Fract. Mech."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Caron, R., Londono, I., Seoud, L., and Villemure, I. (2023). Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method. J. Mech. Behav. Biomed. Mater., 137.","DOI":"10.1016\/j.jmbbm.2022.105540"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Buccino, F., Aiazzi, I., Casto, A., Liu, B., Sbarra, M., Ziarelli, G., Banfi, G., and Vergani, L. (2023). The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage. J. Mech. Behav. Biomed. 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