Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters-Reference-Cited by-同舟云学术

Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters

Published:2022-07 Issue: Volume:160 Page:106840
ISSN:0142-1123
Container-title:International Journal of Fatigue
language:en
Short-container-title:International Journal of Fatigue

Author:

Masoudi Nejad Reza,Sina Nima,Ghahremani Moghadam Danial,Branco Ricardo,Macek Wojciech,Berto Filippo

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,General Materials Science,Modeling and Simulation

Reference64 articles.

1. Simulation of crack propagation of fatigue in Iran rail road wheels and Effect of residual stresses;Masoudi Nejad,2013

2. Experimental and numerical investigation of fatigue crack growth behavior and optimizing fatigue life of riveted joints in Al-alloy 2024 plates;Nejad;Theor Appl Fract Mech,2020

3. Microstructure and fatigue fracture mechanism for a heavy-duty truck diesel engine crankshaft;Aliakbari;Scientia Iranica,2019

4. An applied method for fatigue life assessment of engineering components using rigid-insert crack closure model;Shariati;Eng Fract Mech,2018

5. Influence of welding parameters on fracture toughness and fatigue crack growth rate in friction stir welded nugget of 2024–T351 aluminum alloy joints;Ghahremani moghadam;Trans Nonferrous Met Soc China,2016

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies;Engineering Research Express;2024-07-12

2. Prediction and optimization of joint quality in laser transmission welding using serial artificial neural networks and their integration with Markov decision process;Journal of Laser Applications;2024-07-09

3. The use of polyurethane foam-sand mixtures in sandy embankment design- predicting seismic response using FEM, catastrophe theory, B-spline method, and artificial neural networks;Heliyon;2024-06

4. Prediction and Comparative Analysis of Peak Stress Using Optimized Constitutive and ATPSO-BP Models: A Case Study of 6016H18 Aluminum Alloy;Journal of Electronic Materials;2024-02-20

5. Artificial neural network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters;International Journal of Fatigue;2024-01