Micro-hardness evaluation of the bobbin tool-friction stir welded AA6063 using regression-based machine learning
Author:
Publisher
Elsevier BV
Subject
Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science
Reference8 articles.
1. Effect of material configuration and welding parameter on weld formability and mechanical properties of bobbin tool friction stir welded Al-Cu and Al-Mg aluminum alloys;Wu;Mater. Charact.,2021
2. Microstructure and corrosion behavior of bobbin tool friction stir welded 2219 aluminum alloy;Shao;Mater. Charact.,2022
3. Defect formation, microstructure evolution, and mechanical properties of bobbin tool friction–stir welded 2219–T8 alloy;Wang;Mater. Sci. Eng. A,2022
4. Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random forest regression model;Kwak;J. Mater. Res. Technol.,2022
5. Machine learning approach for predicting the peak temperature of dissimilar AA7050-AA2014A friction stir welding butt joint using various regression models;Anandan;Mater. Lett.,2022
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1. Assessing the Performance of a Dual-Speed Tool When Friction Stir Welding Cast Mg AZ91 with Wrought Al 6082;Materials;2024-07-26
2. Experimental investigation of friction stir welding with special spherical ball shoulder different probe tools and parameters for enhanced material properties of AA2219 aluminium alloy;Welding International;2024-06-28
3. Machine learning metamodels for thermo-mechanical analysis of friction stir welding;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-05-25
4. Maximizing machinability at AA8014 joints by hybrid reinforcement in friction stir processing;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-02-12
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