A machine learning based approach with an augmented dataset for fatigue life prediction of additively manufactured Ti-6Al-4V samples
Author:
Publisher
Elsevier BV
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Reference65 articles.
1. Synchrotron-based X-ray microtomography characterization of the effect of processing variables on porosity formation in laser power-bed additive manufacturing of Ti-6Al-4V;Cunningham;JOM,2017
2. The effect of manufacturing defects on the fatigue life of selective laser melted Ti-6Al-4V structures;Hu;Mater Des,2020
3. Influence of the position and size of various deterministic defects on the high cycle fatigue resistance of a 316L steel manufactured by laser powder bed fusion;Andreau;Int J Fatigue,2021
4. Fatigue performance evaluation of selective laser melted Ti–6Al–4V;Edwards;Mater Sci Engng A,2014
5. Fatigue behaviour of additive manufactured materials: An overview of some recent experimental studies on Ti-6Al-4V considering various processing and loading direction effects;Fatemi;Fatigue Fract Engng Mater Struct,2019
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1. On the generalization capability of artificial neural networks used to estimate fretting fatigue life;Tribology International;2024-04
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