Currently, Giorgio A. B. Oliveira is pursuing a Ph.D. in Mechanical Sciences at University of Brasília, specializing in the field of Fatigue, Fracture, and Materials. Their research focuses on Fretting Fatigue with applications of Artificial Neural Networks. With a strong background in Mechanical Engineering, Giorgio A. B. Oliveira has expertise in various areas including Multiaxial Fatigue, Fretting Fatigue, Artificial Neural Networks, Finite Element Method, Aeronautical Alloys, Overhead Conductors, Composite Materials, and Mechanical Properties.
AbstractTo foresee the life of fretting fatigue with variable amplitude loading, a hybrid model based on non-local multiaxial fatigue parameters and artificial neural networks (ANN) has been proposed. Thus, in order to compute the impact of the variable amplitude loading, the model has been developed based on equivalent stress parameters related to crack nucleation and Miner’s cumulative damage rule. To validate the models, fretting fatigue tests of several different aeronautical alloys were gathered from the literature and used in the ANN training. Furthermore, the analyzed data encompass a wide range of loading conditions and different contact geometries, which contributes to the formulation of a generalized and robust approach for the assessment of fretting fatigue problems. The proposed methodology aims to effectively address the complexities inherent to fretting fatigue under variable amplitude stress. Additionally, these types of fatigue ANN models have been demonstrating a strong capacity for generalization, making them highly promising for future industrial applications.
|Room 7||Wednesday 29th November||17:30-18:00||Giorgio Brito Oliveira|
S05-2 - Contact fatigue & Fatigue in transmission system
117 - A Hybrid ANN Multiaxial Fatigue Model for the Assessment of Fretting Fatigue under Variable Amplitude Shear Loading