|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Biological Materials Science
||NOW ON-DEMAND ONLY - Deep Learning and Finite Element Method towards the Application of Microfracture Analysis for Prevention of Fatigue Fractures in Bones
||Gerardo Presbitero, Marco Hernandez, Inés Hernández-Ferruzca, José Quiroga-Arias, Bibiana González-Pérez, Carlos Mora-Núñez, Eduardo Macías-Ávila, Álvaro Gómez-Ovalle, Christian Mendoza-Buenrostro
|On-Site Speaker (Planned)
We work in establishing a methodology based on the observation of microfractures generated and developed by fatigue and the use of non-destructive testing towards accurate prediction procedures for the prevention of fatigue fractures.
Our studies have focused mainly on studying the growth of microcracks in cortical bones. We aim to confirm this approach for the prediction and prevention of fatigue fractures proposing it applies not only to bones but to industrial and biomedical materials. The methodology establishes a concept called characteristic length, obtained according to the modality in which microfractures grow at the instance of fatigue fracture, in agreement with the Weibull equation of two parameters.
Identification and analysis of microfractures by non-destructive techniques, such as X-ray Computed tomography, and studies we performed with the integration of the concept of characteristic length, will be beneficial for confirming a methodology towards the prevention of fatigue fractures in materials.
||Biomaterials, Mechanical Properties, Polymers