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

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 thr...