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

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

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