On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer
Mechanical properties of fine grain nanocomposites differ from those of conventional composites due to the in situ effect caused by the addition of nanoparticle reinforcement and the complexity of strengthening mechanisms, which make their prediction using conventional analytical and numerical model...
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ftdoajarticles:oai:doaj.org/article:cdd16fc98f2a418d99fb665987866313 2023-05-15T15:41:43+02:00 On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer Ghazi S. Alsoruji A.M. Sadoun Mohamed Abd Elaziz Mohammed Azmi Al-Betar A.W. Abdallah A. Fathy 2023-03-01T00:00:00Z https://doi.org/10.1016/j.jmrt.2023.01.212 https://doaj.org/article/cdd16fc98f2a418d99fb665987866313 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2238785423002144 https://doaj.org/toc/2238-7854 2238-7854 doi:10.1016/j.jmrt.2023.01.212 https://doaj.org/article/cdd16fc98f2a418d99fb665987866313 Journal of Materials Research and Technology, Vol 23, Iss , Pp 4075-4088 (2023) Long-short term memory model Al Beluga whale optimizer Nanocomposite Mechanical properties Mining engineering. Metallurgy TN1-997 article 2023 ftdoajarticles https://doi.org/10.1016/j.jmrt.2023.01.212 2023-02-26T01:32:26Z Mechanical properties of fine grain nanocomposites differ from those of conventional composites due to the in situ effect caused by the addition of nanoparticle reinforcement and the complexity of strengthening mechanisms, which make their prediction using conventional analytical and numerical model is relatively difficult. Therefore, this work presents a rapid reliable machine learning model based on long-short term memory model modified with beluga whale optimizer to predict the mechanical properties of ultrafine grain Al-TiO2 nanocomposite manufactured using accumulative roll bonding (ARB). The mechanical properties were evaluated using tensile tests and correlated with the composite microstructure and hardness. Experimentally, it was demonstrated that the tensile strength increases with increasing the number of ARB passes until a plateau was achieved due to the uniform distribution of TiO2 nanoparticles inside the composite and the saturation of grain refinement in the Al matrix. The maximum tensile achieved was 270 MPa for composite containing 3% TiO2 nanoparticles after 5 ARB passes compared to 90.5 MPa for the raw Al. The proposed model was able to predict the yield and ultimate strengths, elongation, and hardness for all the produced composites tested with excellent accuracy reaching R2 equal 0.9955, 0.9952, 0.9859, and 0.9975, respectively, which is way better than other models. Article in Journal/Newspaper Beluga Beluga whale Beluga* Directory of Open Access Journals: DOAJ Articles Journal of Materials Research and Technology 23 4075 4088 |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Long-short term memory model Al Beluga whale optimizer Nanocomposite Mechanical properties Mining engineering. Metallurgy TN1-997 |
spellingShingle |
Long-short term memory model Al Beluga whale optimizer Nanocomposite Mechanical properties Mining engineering. Metallurgy TN1-997 Ghazi S. Alsoruji A.M. Sadoun Mohamed Abd Elaziz Mohammed Azmi Al-Betar A.W. Abdallah A. Fathy On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer |
topic_facet |
Long-short term memory model Al Beluga whale optimizer Nanocomposite Mechanical properties Mining engineering. Metallurgy TN1-997 |
description |
Mechanical properties of fine grain nanocomposites differ from those of conventional composites due to the in situ effect caused by the addition of nanoparticle reinforcement and the complexity of strengthening mechanisms, which make their prediction using conventional analytical and numerical model is relatively difficult. Therefore, this work presents a rapid reliable machine learning model based on long-short term memory model modified with beluga whale optimizer to predict the mechanical properties of ultrafine grain Al-TiO2 nanocomposite manufactured using accumulative roll bonding (ARB). The mechanical properties were evaluated using tensile tests and correlated with the composite microstructure and hardness. Experimentally, it was demonstrated that the tensile strength increases with increasing the number of ARB passes until a plateau was achieved due to the uniform distribution of TiO2 nanoparticles inside the composite and the saturation of grain refinement in the Al matrix. The maximum tensile achieved was 270 MPa for composite containing 3% TiO2 nanoparticles after 5 ARB passes compared to 90.5 MPa for the raw Al. The proposed model was able to predict the yield and ultimate strengths, elongation, and hardness for all the produced composites tested with excellent accuracy reaching R2 equal 0.9955, 0.9952, 0.9859, and 0.9975, respectively, which is way better than other models. |
format |
Article in Journal/Newspaper |
author |
Ghazi S. Alsoruji A.M. Sadoun Mohamed Abd Elaziz Mohammed Azmi Al-Betar A.W. Abdallah A. Fathy |
author_facet |
Ghazi S. Alsoruji A.M. Sadoun Mohamed Abd Elaziz Mohammed Azmi Al-Betar A.W. Abdallah A. Fathy |
author_sort |
Ghazi S. Alsoruji |
title |
On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer |
title_short |
On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer |
title_full |
On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer |
title_fullStr |
On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer |
title_full_unstemmed |
On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer |
title_sort |
on the prediction of the mechanical properties of ultrafine grain al-tio2 nanocomposites using a modified long-short term memory model with beluga whale optimizer |
publisher |
Elsevier |
publishDate |
2023 |
url |
https://doi.org/10.1016/j.jmrt.2023.01.212 https://doaj.org/article/cdd16fc98f2a418d99fb665987866313 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Journal of Materials Research and Technology, Vol 23, Iss , Pp 4075-4088 (2023) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2238785423002144 https://doaj.org/toc/2238-7854 2238-7854 doi:10.1016/j.jmrt.2023.01.212 https://doaj.org/article/cdd16fc98f2a418d99fb665987866313 |
op_doi |
https://doi.org/10.1016/j.jmrt.2023.01.212 |
container_title |
Journal of Materials Research and Technology |
container_volume |
23 |
container_start_page |
4075 |
op_container_end_page |
4088 |
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1766374603215601664 |