Experimental investigation and machine learning modeling using LSTM and special relativity search of friction stir processed AA2024/Al2O3 nanocomposites-Reference-Cited by-同舟云学术

Experimental investigation and machine learning modeling using LSTM and special relativity search of friction stir processed AA2024/Al2O3 nanocomposites

Published:2023-11 Issue: Volume:27 Page:7442-7456
ISSN:2238-7854
Container-title:Journal of Materials Research and Technology
language:en
Short-container-title:Journal of Materials Research and Technology

Author:

Djouider Fathi,Elaziz Mohamed Abd,Alhawsawi Abdulsalam,Banoqitah Essam,Moustafa Essam B.,Elsheikh Ammar H.^ORCID

Publisher

Elsevier BV

Subject

Metals and Alloys,Surfaces, Coatings and Films,Biomaterials,Ceramics and Composites

Reference77 articles.

1. Through-thickness inhomogeneity in microstructure and tensile properties and tribological performance of friction stir processed AA1050-Al2O3 nanocomposite;Bourkhani;Composites Part B,2019

2. An optimized multilayer perceptrons model using grey wolf optimizer to predict mechanical and microstructural properties of friction stir processed aluminum alloy reinforced by nanoparticles;Khoshaim;Coatings,2021

3. Effects of pin diameter and number of cycles on microstructure and tensile properties of friction stir fabricated AA1050-Al2O3 nanocomposite;Darzi Bourkhani;J Mater Res Technol,2020

4. Relating grain size to the zener–hollomon parameter for twin-roll-cast AZ31B alloy refined by friction stir processing;Ammouri;J Mater Process Technol,2015

5. A future prospects and current scenario of aluminium metal matrix composites characteristics;Singh;Alex Eng J,2023

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