Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

Author:

Sami Abdullatef Mustafa,N. Alzubaidi Faten,Al-Tamimi Anees,Ahmed Mahmood Yasser

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

General Materials Science

Reference22 articles.

1. Fatigue modeling using neural networks: A comprehensive review;Chen;Fatigue & Fracture of Engineering Materials & Structure,2022

2. Prediction fatigue life of aluminum alloy 7075 T73 using neural networks and neuro-fuzzy models;Abdullatef;Engineering and Technology Journal,2016

3. Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network;Barbosa;International Journal of Fatigue,2020

4. Fatigue damage detection of aerospace-grade aluminum alloys using feature-based and feature-less deep neural networks;Dharmadhikari;Machine Learning with Applications,2022

5. Application of artificial neural network for predicting fatigue crack propagation life of aluminum alloys;Mohanty;Computational Materials Science and Surface Engineering,2009

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