Prediction of the tensile properties of ultrafine grained Al–SiC nanocomposites using machine learning
We discovered and analyzed the new prediction model by using machine learning (ML) for the tensile strength of aluminum nanocomposites reinforced with μ-SiC particles fabricated by accumulative roll bonding (ARB). The effect of the number of cycles and SiC content on the microstructure, phase analys...
Saved in:
| Published in: | Journal of materials research and technology Vol. 24; pp. 7666 - 7682 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.05.2023
Elsevier |
| Subjects: | |
| ISSN: | 2238-7854 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | We discovered and analyzed the new prediction model by using machine learning (ML) for the tensile strength of aluminum nanocomposites reinforced with μ-SiC particles fabricated by accumulative roll bonding (ARB). The effect of the number of cycles and SiC content on the microstructure, phase analysis, tensile, and hardness properties have been investigated for the ARBed sheets and their composites. The experimental results showed the distribution of SiC particles improved by increasing ARB passes. The ARB approach greatly enhanced the ultimate tensile strength (UTS), yield strength (YS), and hardness. The UTS achieved was 254 MPa for 4% SiC after 9 ARB cycles. The hardness values of the ARBed AA1050, and AA1050-4 wt% SiC are 60, and 76.5, respectively, after 9 ARB cycles. The modified version of random vector functional link based on Growth Optimizer Algorithm is developed as a machine-learning model to predict the tensile properties of the produced composites. The efficiency of the developed ML model is evaluated with other methods according to the performance criteria. |
|---|---|
| AbstractList | We discovered and analyzed the new prediction model by using machine learning (ML) for the tensile strength of aluminum nanocomposites reinforced with μ-SiC particles fabricated by accumulative roll bonding (ARB). The effect of the number of cycles and SiC content on the microstructure, phase analysis, tensile, and hardness properties have been investigated for the ARBed sheets and their composites. The experimental results showed the distribution of SiC particles improved by increasing ARB passes. The ARB approach greatly enhanced the ultimate tensile strength (UTS), yield strength (YS), and hardness. The UTS achieved was 254 MPa for 4% SiC after 9 ARB cycles. The hardness values of the ARBed AA1050, and AA1050-4 wt% SiC are 60, and 76.5, respectively, after 9 ARB cycles. The modified version of random vector functional link based on Growth Optimizer Algorithm is developed as a machine-learning model to predict the tensile properties of the produced composites. The efficiency of the developed ML model is evaluated with other methods according to the performance criteria. |
| Author | Elaziz, Mohamed Abd Fathy, A. Ahmadian, H. Kabeel, A.M. Najjar, I.M.R. Sadoun, A.M. |
| Author_xml | – sequence: 1 givenname: I.M.R. surname: Najjar fullname: Najjar, I.M.R. email: inajjar@kau.edu.sa organization: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia – sequence: 2 givenname: A.M. surname: Sadoun fullname: Sadoun, A.M. organization: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia – sequence: 3 givenname: Mohamed Abd surname: Elaziz fullname: Elaziz, Mohamed Abd organization: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt – sequence: 4 givenname: H. surname: Ahmadian fullname: Ahmadian, H. organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, People's Republic of China – sequence: 5 givenname: A. surname: Fathy fullname: Fathy, A. email: adelfathy99@yahoo.com organization: Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt – sequence: 6 givenname: A.M. surname: Kabeel fullname: Kabeel, A.M. organization: Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt |
| BookMark | eNp9kc2KFDEQx3NYwXV2X8BTv8C0Seej0-BlGfxYWHBBPYdMpTKTpicZkqzgzXfwDX0S04548LCnIlX1-0P98opcxRSRkNeM9owy9Wbu51Ou_UAH3lPZUy6vyPUwcL0dtRQvyW0pM6WUyUlRza7J8TGjC1BDil3yXT1iVzGWsGB3zumMuQYs6-Rpqdn6ELE7ZNuK6-6WXz9-fg67LtqYIJ3OqYTalp9KiIfuZOG4bi9oc2yNG_LC26Xg7d-6IV_fv_uy-7h9-PThfnf3sAXBaN16vUcYlVKeT1ziXgvqlIUREKwTisHEQAOVKEY5jhw09X5QToETnsFe8A25v-S6ZGdzzuFk83eTbDB_GikfjG1HwYJmUpNQkuPE9CCk5JN1XDOhpv3onW-PDRkuWZBTKRn9vzxGzarbzGbVbVbdhkrTdDdI_wdBqHYV3ASG5Xn07QXFJuhbwGwKBIzQfigj1HZBeA7_DbMgop8 |
| CitedBy_id | crossref_primary_10_1016_j_diamond_2024_111113 crossref_primary_10_1016_j_jallcom_2025_179142 crossref_primary_10_1016_j_mtcomm_2025_113777 crossref_primary_10_1007_s11665_025_11327_x crossref_primary_10_1016_j_msea_2023_145816 crossref_primary_10_1038_s41598_024_67004_x crossref_primary_10_1016_j_jmatprotec_2024_118596 crossref_primary_10_1177_00219983231186205 crossref_primary_10_1016_j_jmrt_2025_08_113 crossref_primary_10_1016_j_vacuum_2024_113484 crossref_primary_10_1515_ijmr_2021_8412 crossref_primary_10_1016_j_ceramint_2024_08_352 crossref_primary_10_1177_09544062251349011 crossref_primary_10_1016_j_oceaneng_2024_118320 crossref_primary_10_1088_2051_672X_adabfe crossref_primary_10_1016_j_mtcomm_2024_109303 crossref_primary_10_1016_j_powtec_2024_119651 crossref_primary_10_1016_j_jallcom_2024_176532 crossref_primary_10_3390_ma16175927 crossref_primary_10_1002_masy_202300164 crossref_primary_10_1002_pat_70057 crossref_primary_10_1016_j_msea_2024_146892 crossref_primary_10_1016_j_jestch_2024_101897 crossref_primary_10_1142_S0219876225500057 crossref_primary_10_1016_j_triboint_2025_111148 crossref_primary_10_1007_s11665_025_11380_6 crossref_primary_10_1016_j_compositesa_2025_109027 crossref_primary_10_3389_feart_2025_1645393 crossref_primary_10_1007_s11837_024_07119_8 crossref_primary_10_1007_s00170_024_14070_0 crossref_primary_10_1007_s10853_025_11372_w crossref_primary_10_1007_s11665_024_10045_0 crossref_primary_10_1016_j_vacuum_2024_113899 crossref_primary_10_1016_j_mtcomm_2023_107835 crossref_primary_10_1016_j_rineng_2024_103471 crossref_primary_10_1016_j_jmatprotec_2025_118821 crossref_primary_10_1016_j_mtcomm_2024_109279 crossref_primary_10_1016_j_cirpj_2025_06_020 |
| Cites_doi | 10.1016/j.matdes.2014.01.022 10.1016/j.msea.2018.12.090 10.3390/math10081266 10.1177/0021998319876684 10.1016/j.jallcom.2012.10.069 10.1016/j.jallcom.2018.04.167 10.1016/j.mtcomm.2023.105743 10.1007/s12613-019-1917-3 10.1016/j.msea.2011.03.061 10.1016/j.scriptamat.2008.06.040 10.1016/j.scriptamat.2020.03.064 10.1016/j.jmrt.2023.01.212 10.1016/j.mtla.2020.100699 10.1177/0021998318781462 10.1016/j.rinp.2019.102911 10.1016/j.mechmat.2020.103321 10.1016/j.ceramint.2018.10.147 10.1016/j.aej.2022.09.036 10.4236/ojmetal.2011.12004 10.1016/j.jmsy.2022.08.014 10.3390/app9173627 10.1177/0021998319851831 10.1007/s00521-019-04663-2 10.1016/j.jmst.2020.08.008 10.1016/j.powtec.2023.118291 10.1016/j.jmrt.2021.10.111 10.1016/j.jallcom.2022.163828 10.1016/j.diamond.2021.108755 10.1016/j.msea.2014.04.013 10.1016/j.rinp.2019.102814 10.1038/s41524-019-0221-0 10.1016/j.ceramint.2021.11.322 10.1016/j.jallcom.2017.05.209 10.1088/2053-1591/ab9d53 10.1016/j.matdes.2013.04.032 10.1007/s11661-011-1039-7 10.1177/0021998320934860 10.3390/lubricants10110277 |
| ContentType | Journal Article |
| Copyright | 2023 The Author(s) |
| Copyright_xml | – notice: 2023 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION DOA |
| DOI | 10.1016/j.jmrt.2023.05.035 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EndPage | 7682 |
| ExternalDocumentID | oai_doaj_org_article_9694653e918245539ad381469b7fdfad 10_1016_j_jmrt_2023_05_035 S2238785423010001 |
| GroupedDBID | 0R~ 0SF 4.4 457 5VS 6I. AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAXUO ABMAC ABXRA ACGFS ADBBV ADCUG ADEZE AEXQZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV EBS EJD FDB FNPLU GROUPED_DOAJ GX1 HH5 HZ~ IPNFZ IXB KQ8 M41 NCXOZ O9- OK1 RIG ROL SSZ AAYWO AAYXX ADVLN AFJKZ CITATION |
| ID | FETCH-LOGICAL-c410t-f8bec7666f3935eb840d6ac7cecad461c91c8c05e475773c80ff26d6cd4f1cb43 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 52 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001030065500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2238-7854 |
| IngestDate | Fri Oct 03 12:41:18 EDT 2025 Thu Nov 13 04:14:55 EST 2025 Tue Nov 18 21:58:00 EST 2025 Sat Sep 30 17:11:33 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Hardness Al– SiC nanocomposite Accumulative roll bonding Tensile strength Machine learning Growth optimizer algorithm |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c410t-f8bec7666f3935eb840d6ac7cecad461c91c8c05e475773c80ff26d6cd4f1cb43 |
| OpenAccessLink | https://doaj.org/article/9694653e918245539ad381469b7fdfad |
| PageCount | 17 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_9694653e918245539ad381469b7fdfad crossref_primary_10_1016_j_jmrt_2023_05_035 crossref_citationtrail_10_1016_j_jmrt_2023_05_035 elsevier_sciencedirect_doi_10_1016_j_jmrt_2023_05_035 |
| PublicationCentury | 2000 |
| PublicationDate | May-June 2023 2023-05-00 2023-05-01 |
| PublicationDateYYYYMMDD | 2023-05-01 |
| PublicationDate_xml | – month: 05 year: 2023 text: May-June 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of materials research and technology |
| PublicationYear | 2023 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Peng, Cheng, Zhang, Shao, Wang, Shen (bib25) 2022; 65 Wang, Wang, Wang, Hu, Wu, Wang (bib16) 2014; 57 Sadoun, Meselhy, Deabs (bib26) 2020; 16 Darmiani, Danaee, Golozar, Toroghinejad (bib35) 2013; 552 Mahallawy, Fathy, Hassan, Abdelaziem (bib1) 2017; 0021998317692141 Wagih, Fathy, Ibrahim, Elkady, Hassan (bib3) 2018; 752 Ghalehbandi, Malaki, Gupta (bib6) 2019; 9 Li, Wang, Zhang, Li, Gao, Sun (bib7) 2019; 745 Ahmadian, Sallakhniknezhad, Zhou, Kiahosseini (bib32) 2022; 121 Schmidt, Marques, Botti, Marques (bib28) 2019; 5 Reihanian, Hadadian, Paydar (bib5) 2014; 607 Nie, Wang, Hu, Xu, Wu, Zheng (bib17) 2011; 528 Hu, Lai, Du, Ho, Huang (bib8) 2008; 59 Kim, Lee, Lee, Son, Reddy, Kim (bib21) 2020; 11 Fathy, Wagih, Abu-Oqail (bib33) 2019; 45 Jiang, Jia, Zhang, Zhang, Wang, Zhang (bib22) 2020; 186 Rezayat, Akbarzadeh, Owhadi (bib30) 2012; 43 Fathy, Ibrahim, Elkady, Hassan (bib37) 2019; 53 Sadoun, Najjar, Fathy, Abd Elaziz, Al-qaness, Abdallah, Elmahdy (bib40) 2023; 65 Kiahosseini, Ahmadian (bib2) 2020; 27 Meselhy, Reda (bib11) 2019; 53 Elwan, Fathy, Wagih, Essa, Abu-Oqail, EL-Nikhaily (bib15) 2020; 54 Amirkhanlou, Ketabchi, Parvin, Khorsand, Bahrami (bib14) 2013; 51 Najjar, Sadoun, Fathy, Abdallah, Elaziz, Elmahdy (bib39) 2022; 10 Asteris, Apostolopoulou, Skentou, Moropoulou (bib19) 2019; 24 Ahmadian, Sadoun, Fathy, Zhou (bib41) 2023; 418 Park, Kayani, Euh, Seo, Kim, Park (bib29) 2022; 903 Fathy, Elkady, Abu-Oqail (bib31) 2017; 719 Du, Feng, Zhang (bib27) 2021; 15 Mohamed, Mohammed, Ibrahim, El-Kady (bib12) 2020; 54 Shaat, Fathy, Wagih (bib38) 2020; 143 Sadoun, Najjar, Alsoruji, Abd-Elwahed, Elaziz, Fathy (bib13) 2022; 10 Li, Xie, Fang, Liu, Liu, Liaw (bib23) 2021; 68 Najjar, Sadoun, Alsoruji, Abd Elaziz, Wagih (bib34) 2022; 48 Sadoun, Wagih, Fathy, Essa (bib18) 2019; 15 Asteris, Mokos (bib20) 2020; 32 Hassanein, Sadoun, Abu-Oqail (bib36) 2020; 7 Najjar, Sadoun, Alam, Fathy (bib9) 2023 Chawla, Chawla, KK (bib4) 2006 Alsoruji, Sadoun, Abd Elaziz, Al-Betar, Abdallah, Fathy (bib10) 2023; 23 Shehata, Abdelhameed, Fathy, Elmahdy (bib24) 2011; 1 Mahallawy (10.1016/j.jmrt.2023.05.035_bib1) 2017; 0021998317692141 Reihanian (10.1016/j.jmrt.2023.05.035_bib5) 2014; 607 Asteris (10.1016/j.jmrt.2023.05.035_bib19) 2019; 24 Li (10.1016/j.jmrt.2023.05.035_bib23) 2021; 68 Shehata (10.1016/j.jmrt.2023.05.035_bib24) 2011; 1 Kiahosseini (10.1016/j.jmrt.2023.05.035_bib2) 2020; 27 Shaat (10.1016/j.jmrt.2023.05.035_bib38) 2020; 143 Elwan (10.1016/j.jmrt.2023.05.035_bib15) 2020; 54 Fathy (10.1016/j.jmrt.2023.05.035_bib37) 2019; 53 Wagih (10.1016/j.jmrt.2023.05.035_bib3) 2018; 752 Alsoruji (10.1016/j.jmrt.2023.05.035_bib10) 2023; 23 Sadoun (10.1016/j.jmrt.2023.05.035_bib26) 2020; 16 Schmidt (10.1016/j.jmrt.2023.05.035_bib28) 2019; 5 Chawla (10.1016/j.jmrt.2023.05.035_bib4) 2006 Fathy (10.1016/j.jmrt.2023.05.035_bib31) 2017; 719 Darmiani (10.1016/j.jmrt.2023.05.035_bib35) 2013; 552 Fathy (10.1016/j.jmrt.2023.05.035_bib33) 2019; 45 Asteris (10.1016/j.jmrt.2023.05.035_bib20) 2020; 32 Mohamed (10.1016/j.jmrt.2023.05.035_bib12) 2020; 54 Park (10.1016/j.jmrt.2023.05.035_bib29) 2022; 903 Sadoun (10.1016/j.jmrt.2023.05.035_bib40) 2023; 65 Najjar (10.1016/j.jmrt.2023.05.035_bib39) 2022; 10 Ghalehbandi (10.1016/j.jmrt.2023.05.035_bib6) 2019; 9 Ahmadian (10.1016/j.jmrt.2023.05.035_bib32) 2022; 121 Sadoun (10.1016/j.jmrt.2023.05.035_bib18) 2019; 15 Najjar (10.1016/j.jmrt.2023.05.035_bib34) 2022; 48 Hu (10.1016/j.jmrt.2023.05.035_bib8) 2008; 59 Li (10.1016/j.jmrt.2023.05.035_bib7) 2019; 745 Rezayat (10.1016/j.jmrt.2023.05.035_bib30) 2012; 43 Du (10.1016/j.jmrt.2023.05.035_bib27) 2021; 15 Kim (10.1016/j.jmrt.2023.05.035_bib21) 2020; 11 Sadoun (10.1016/j.jmrt.2023.05.035_bib13) 2022; 10 Najjar (10.1016/j.jmrt.2023.05.035_bib9) 2023 Amirkhanlou (10.1016/j.jmrt.2023.05.035_bib14) 2013; 51 Ahmadian (10.1016/j.jmrt.2023.05.035_bib41) 2023; 418 Peng (10.1016/j.jmrt.2023.05.035_bib25) 2022; 65 Wang (10.1016/j.jmrt.2023.05.035_bib16) 2014; 57 Jiang (10.1016/j.jmrt.2023.05.035_bib22) 2020; 186 Meselhy (10.1016/j.jmrt.2023.05.035_bib11) 2019; 53 Hassanein (10.1016/j.jmrt.2023.05.035_bib36) 2020; 7 Nie (10.1016/j.jmrt.2023.05.035_bib17) 2011; 528 |
| References_xml | – year: 2023 ident: bib9 article-title: Prediction of wear rates of Al-TiO2 nanocomposites using artificial neural network modified with particle swarm optimization algorithm publication-title: Mater Today Commun – volume: 53 start-page: 209 year: 2019 end-page: 218 ident: bib37 article-title: Evaluation of mechanical properties of 1050-Al reinforced with SiC particles via accumulative roll bonding process publication-title: J Compos Mater – volume: 121 start-page: 108755 year: 2022 ident: bib32 article-title: Mechanical properties of Al-Mg/MWCNT nanocomposite powder produced under different parameters of ball milling process publication-title: Diam Relat Mater – volume: 607 start-page: 188 year: 2014 end-page: 196 ident: bib5 article-title: Fabrication of Al–2 vol% Al2O3/SiC hybrid composite via accumulative roll bonding (ARB): an investigation of the microstructure and mechanical properties publication-title: Mater Sci Eng, A – volume: 15 start-page: 4914 year: 2021 end-page: 4930 ident: bib27 article-title: Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy publication-title: J Mater Res Technol – volume: 1 start-page: 25 year: 2011 ident: bib24 article-title: Preparation and characteristics of Cu-Al 2 O 3 nanocomposite publication-title: Open J Met – volume: 9 start-page: 3627 year: 2019 ident: bib6 article-title: Accumulative roll bonding—a review publication-title: Appl Sci – volume: 10 year: 2022 ident: bib13 article-title: Utilization of improved machine learning method based on artificial hummingbird algorithm to predict the tribological behavior of Cu-Al2O3 nanocomposites synthesized by in situ method publication-title: Mathematics – volume: 54 start-page: 1259 year: 2020 end-page: 1271 ident: bib15 article-title: Fabrication and investigation on the properties of ilmenite (FeTiO3)-based Al composite by accumulative roll bonding publication-title: J Compos Mater – volume: 719 start-page: 411 year: 2017 end-page: 419 ident: bib31 article-title: Synthesis and characterization of Cu–ZrO2 nanocomposite produced by thermochemical process publication-title: J Alloys Compd – volume: 45 start-page: 2319 year: 2019 end-page: 2329 ident: bib33 article-title: Effect of ZrO2 content on properties of Cu-ZrO2 nanocomposites synthesized by optimized high energy ball milling publication-title: Ceram Int – volume: 59 start-page: 1163 year: 2008 end-page: 1166 ident: bib8 article-title: Enhanced tensile plasticity in ultrafine-grained metallic composite fabricated by friction stir process publication-title: Scripta Mater – volume: 528 start-page: 5278 year: 2011 end-page: 5282 ident: bib17 article-title: Microstructure and mechanical properties of SiC nanoparticles reinforced magnesium matrix composites fabricated by ultrasonic vibration publication-title: Mater Sci Eng, A – volume: 48 start-page: 7748 year: 2022 end-page: 7758 ident: bib34 article-title: Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments publication-title: Ceram Int – volume: 65 start-page: 809 year: 2023 end-page: 823 ident: bib40 article-title: An enhanced Dendritic Neural Algorithm to predict the wear behavior of alumina coated silver reinforced copper nanocomposites publication-title: Alex Eng J – volume: 418 year: 2023 ident: bib41 article-title: Utilizing a unified conceptual dynamic model for prediction of particle size of duel-matrix nanocomposites during mechanical alloying publication-title: Powder Technol – volume: 7 year: 2020 ident: bib36 article-title: Effect of SiC addition on the mechanical properties and wear behavior of Al-SiC nanocomposites produced by accumulative roll bonding publication-title: Mater Res Express – volume: 10 start-page: 277 year: 2022 ident: bib39 article-title: Prediction of tribological properties of alumina-coated, silver-reinforced copper nanocomposites using long short-term model combined with golden jackal optimization publication-title: Lubricants – volume: 11 start-page: 100699 year: 2020 ident: bib21 article-title: Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels publication-title: Materialia – volume: 0021998317692141 year: 2017 ident: bib1 article-title: Evaluation of mechanical properties and microstructure of Al/Al–12% Si multilayer via warm accumulative roll bonding process publication-title: J Compos Mater – volume: 53 start-page: 3951 year: 2019 end-page: 3961 ident: bib11 article-title: Investigation of mechanical properties of nanostructured Al-SiC composite manufactured by accumulative roll bonding publication-title: J Compos Mater – volume: 16 year: 2020 ident: bib26 article-title: Improved strength and ductility of friction stir tailor-welded blanks of base metal AA2024 reinforced with interlayer strip of AA7075 publication-title: Results Phys – volume: 186 start-page: 272 year: 2020 end-page: 277 ident: bib22 article-title: A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data publication-title: Scripta Mater – volume: 24 start-page: 329 year: 2019 end-page: 345 ident: bib19 article-title: Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars publication-title: Comput Concr – volume: 752 start-page: 137 year: 2018 end-page: 147 ident: bib3 article-title: Experimental investigation on strengthening mechanisms in Al-SiC nanocomposites and 3D FE simulation of Vickers indentation publication-title: J Alloys Compd – volume: 43 start-page: 2085 year: 2012 end-page: 2093 ident: bib30 article-title: Fabrication of high-strength Al/Sicp nanocomposite sheets by accumulative roll bonding publication-title: Metall Mater Trans A Phys Metall Mater Sci – volume: 27 start-page: 384 year: 2020 end-page: 390 ident: bib2 article-title: Effect of residual structural strain caused by the addition of Co3O4 nanoparticles on the structural, hardness and magnetic properties of an Al/Co3O4 nanocomposite produced by powder metallurgy publication-title: Int J Miner Metall Mater – volume: 32 start-page: 11807 year: 2020 end-page: 11826 ident: bib20 article-title: Concrete compressive strength using artificial neural networks publication-title: Neural Comput Appl – volume: 51 start-page: 367 year: 2013 end-page: 374 ident: bib14 article-title: Accumulative press bonding; a novel manufacturing process of nanostructured metal matrix composites publication-title: Mater Des – volume: 15 year: 2019 ident: bib18 article-title: Effect of tool pin side area ratio on temperature distribution in friction stir welding publication-title: Results Phys – volume: 65 start-page: 104 year: 2022 end-page: 114 ident: bib25 article-title: Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method publication-title: J Manuf Syst – volume: 903 start-page: 163828 year: 2022 ident: bib29 article-title: H.High strength aluminum alloys design via explainable artificial intelligence publication-title: J Alloys Compd – volume: 745 start-page: 10 year: 2019 end-page: 19 ident: bib7 article-title: Enhanced combination of strength and ductility in ultrafine-grained aluminum composites reinforced with high content intragranular nanoparticles publication-title: Mater Sci Eng, A – volume: 57 start-page: 638 year: 2014 end-page: 645 ident: bib16 article-title: Processing, microstructure and mechanical properties of micro-SiC particles reinforced magnesium matrix composites fabricated by stir casting assisted by ultrasonic treatment processing publication-title: Mater Des – volume: 68 start-page: 70 year: 2021 end-page: 75 ident: bib23 article-title: High-throughput simulation combined machine learning search for optimum elemental composition in medium entropy alloy publication-title: J Mater Sci Technol – volume: 54 start-page: 4921 year: 2020 end-page: 4928 ident: bib12 article-title: Effect of nano Al2O3 coated Ag reinforced Cu matrix nanocomposites on mechanical and tribological behavior synthesis by P/M technique publication-title: J Compos Mater – volume: 143 year: 2020 ident: bib38 article-title: Correlation between grain boundary evolution and mechanical properties of ultrafine-grained metals publication-title: Mech Mater – volume: 552 start-page: 31 year: 2013 end-page: 39 ident: bib35 article-title: Corrosion investigation of Al–SiC nano-composite fabricated by accumulative roll bonding (ARB) process publication-title: J Alloys Compd – volume: 5 start-page: 1 year: 2019 end-page: 36 ident: bib28 article-title: Recent advances and applications of machine learning in solid-state materials science publication-title: npj Comput Mater – volume: 23 start-page: 4075 year: 2023 end-page: 4088 ident: bib10 article-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 publication-title: J Mater Res Technol – start-page: 172 year: 2006 ident: bib4 article-title: Metal matrix composites. Chou, TW structure and properties of composites – volume: 57 start-page: 638 year: 2014 ident: 10.1016/j.jmrt.2023.05.035_bib16 article-title: Processing, microstructure and mechanical properties of micro-SiC particles reinforced magnesium matrix composites fabricated by stir casting assisted by ultrasonic treatment processing publication-title: Mater Des doi: 10.1016/j.matdes.2014.01.022 – volume: 745 start-page: 10 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib7 article-title: Enhanced combination of strength and ductility in ultrafine-grained aluminum composites reinforced with high content intragranular nanoparticles publication-title: Mater Sci Eng, A doi: 10.1016/j.msea.2018.12.090 – volume: 10 year: 2022 ident: 10.1016/j.jmrt.2023.05.035_bib13 article-title: Utilization of improved machine learning method based on artificial hummingbird algorithm to predict the tribological behavior of Cu-Al2O3 nanocomposites synthesized by in situ method publication-title: Mathematics doi: 10.3390/math10081266 – volume: 54 start-page: 1259 issue: 10 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib15 article-title: Fabrication and investigation on the properties of ilmenite (FeTiO3)-based Al composite by accumulative roll bonding publication-title: J Compos Mater doi: 10.1177/0021998319876684 – volume: 552 start-page: 31 year: 2013 ident: 10.1016/j.jmrt.2023.05.035_bib35 article-title: Corrosion investigation of Al–SiC nano-composite fabricated by accumulative roll bonding (ARB) process publication-title: J Alloys Compd doi: 10.1016/j.jallcom.2012.10.069 – volume: 752 start-page: 137 year: 2018 ident: 10.1016/j.jmrt.2023.05.035_bib3 article-title: Experimental investigation on strengthening mechanisms in Al-SiC nanocomposites and 3D FE simulation of Vickers indentation publication-title: J Alloys Compd doi: 10.1016/j.jallcom.2018.04.167 – year: 2023 ident: 10.1016/j.jmrt.2023.05.035_bib9 article-title: Prediction of wear rates of Al-TiO2 nanocomposites using artificial neural network modified with particle swarm optimization algorithm publication-title: Mater Today Commun doi: 10.1016/j.mtcomm.2023.105743 – volume: 27 start-page: 384 issue: 3 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib2 article-title: Effect of residual structural strain caused by the addition of Co3O4 nanoparticles on the structural, hardness and magnetic properties of an Al/Co3O4 nanocomposite produced by powder metallurgy publication-title: Int J Miner Metall Mater doi: 10.1007/s12613-019-1917-3 – volume: 528 start-page: 5278 issue: 15 year: 2011 ident: 10.1016/j.jmrt.2023.05.035_bib17 article-title: Microstructure and mechanical properties of SiC nanoparticles reinforced magnesium matrix composites fabricated by ultrasonic vibration publication-title: Mater Sci Eng, A doi: 10.1016/j.msea.2011.03.061 – volume: 59 start-page: 1163 issue: 11 year: 2008 ident: 10.1016/j.jmrt.2023.05.035_bib8 article-title: Enhanced tensile plasticity in ultrafine-grained metallic composite fabricated by friction stir process publication-title: Scripta Mater doi: 10.1016/j.scriptamat.2008.06.040 – volume: 186 start-page: 272 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib22 article-title: A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data publication-title: Scripta Mater doi: 10.1016/j.scriptamat.2020.03.064 – volume: 23 start-page: 4075 year: 2023 ident: 10.1016/j.jmrt.2023.05.035_bib10 article-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 publication-title: J Mater Res Technol doi: 10.1016/j.jmrt.2023.01.212 – volume: 11 start-page: 100699 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib21 article-title: Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels publication-title: Materialia doi: 10.1016/j.mtla.2020.100699 – volume: 53 start-page: 209 issue: 2 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib37 article-title: Evaluation of mechanical properties of 1050-Al reinforced with SiC particles via accumulative roll bonding process publication-title: J Compos Mater doi: 10.1177/0021998318781462 – volume: 16 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib26 article-title: Improved strength and ductility of friction stir tailor-welded blanks of base metal AA2024 reinforced with interlayer strip of AA7075 publication-title: Results Phys doi: 10.1016/j.rinp.2019.102911 – volume: 143 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib38 article-title: Correlation between grain boundary evolution and mechanical properties of ultrafine-grained metals publication-title: Mech Mater doi: 10.1016/j.mechmat.2020.103321 – volume: 45 start-page: 2319 issue: 2 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib33 article-title: Effect of ZrO2 content on properties of Cu-ZrO2 nanocomposites synthesized by optimized high energy ball milling publication-title: Ceram Int doi: 10.1016/j.ceramint.2018.10.147 – volume: 24 start-page: 329 issue: 4 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib19 article-title: Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars publication-title: Comput Concr – volume: 65 start-page: 809 year: 2023 ident: 10.1016/j.jmrt.2023.05.035_bib40 article-title: An enhanced Dendritic Neural Algorithm to predict the wear behavior of alumina coated silver reinforced copper nanocomposites publication-title: Alex Eng J doi: 10.1016/j.aej.2022.09.036 – volume: 1 start-page: 25 issue: 2 year: 2011 ident: 10.1016/j.jmrt.2023.05.035_bib24 article-title: Preparation and characteristics of Cu-Al 2 O 3 nanocomposite publication-title: Open J Met doi: 10.4236/ojmetal.2011.12004 – volume: 65 start-page: 104 year: 2022 ident: 10.1016/j.jmrt.2023.05.035_bib25 article-title: Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2022.08.014 – volume: 9 start-page: 3627 issue: 17 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib6 article-title: Accumulative roll bonding—a review publication-title: Appl Sci doi: 10.3390/app9173627 – volume: 53 start-page: 3951 issue: 28–30 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib11 article-title: Investigation of mechanical properties of nanostructured Al-SiC composite manufactured by accumulative roll bonding publication-title: J Compos Mater doi: 10.1177/0021998319851831 – volume: 32 start-page: 11807 issue: 15 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib20 article-title: Concrete compressive strength using artificial neural networks publication-title: Neural Comput Appl doi: 10.1007/s00521-019-04663-2 – volume: 0021998317692141 year: 2017 ident: 10.1016/j.jmrt.2023.05.035_bib1 article-title: Evaluation of mechanical properties and microstructure of Al/Al–12% Si multilayer via warm accumulative roll bonding process publication-title: J Compos Mater – volume: 68 start-page: 70 year: 2021 ident: 10.1016/j.jmrt.2023.05.035_bib23 article-title: High-throughput simulation combined machine learning search for optimum elemental composition in medium entropy alloy publication-title: J Mater Sci Technol doi: 10.1016/j.jmst.2020.08.008 – volume: 418 year: 2023 ident: 10.1016/j.jmrt.2023.05.035_bib41 article-title: Utilizing a unified conceptual dynamic model for prediction of particle size of duel-matrix nanocomposites during mechanical alloying publication-title: Powder Technol doi: 10.1016/j.powtec.2023.118291 – volume: 15 start-page: 4914 year: 2021 ident: 10.1016/j.jmrt.2023.05.035_bib27 article-title: Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy publication-title: J Mater Res Technol doi: 10.1016/j.jmrt.2021.10.111 – volume: 903 start-page: 163828 year: 2022 ident: 10.1016/j.jmrt.2023.05.035_bib29 article-title: H.High strength aluminum alloys design via explainable artificial intelligence publication-title: J Alloys Compd doi: 10.1016/j.jallcom.2022.163828 – start-page: 172 year: 2006 ident: 10.1016/j.jmrt.2023.05.035_bib4 – volume: 121 start-page: 108755 year: 2022 ident: 10.1016/j.jmrt.2023.05.035_bib32 article-title: Mechanical properties of Al-Mg/MWCNT nanocomposite powder produced under different parameters of ball milling process publication-title: Diam Relat Mater doi: 10.1016/j.diamond.2021.108755 – volume: 607 start-page: 188 year: 2014 ident: 10.1016/j.jmrt.2023.05.035_bib5 article-title: Fabrication of Al–2 vol% Al2O3/SiC hybrid composite via accumulative roll bonding (ARB): an investigation of the microstructure and mechanical properties publication-title: Mater Sci Eng, A doi: 10.1016/j.msea.2014.04.013 – volume: 15 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib18 article-title: Effect of tool pin side area ratio on temperature distribution in friction stir welding publication-title: Results Phys doi: 10.1016/j.rinp.2019.102814 – volume: 5 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.jmrt.2023.05.035_bib28 article-title: Recent advances and applications of machine learning in solid-state materials science publication-title: npj Comput Mater doi: 10.1038/s41524-019-0221-0 – volume: 48 start-page: 7748 issue: 6 year: 2022 ident: 10.1016/j.jmrt.2023.05.035_bib34 article-title: Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments publication-title: Ceram Int doi: 10.1016/j.ceramint.2021.11.322 – volume: 719 start-page: 411 year: 2017 ident: 10.1016/j.jmrt.2023.05.035_bib31 article-title: Synthesis and characterization of Cu–ZrO2 nanocomposite produced by thermochemical process publication-title: J Alloys Compd doi: 10.1016/j.jallcom.2017.05.209 – volume: 7 issue: 7 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib36 article-title: Effect of SiC addition on the mechanical properties and wear behavior of Al-SiC nanocomposites produced by accumulative roll bonding publication-title: Mater Res Express doi: 10.1088/2053-1591/ab9d53 – volume: 51 start-page: 367 year: 2013 ident: 10.1016/j.jmrt.2023.05.035_bib14 article-title: Accumulative press bonding; a novel manufacturing process of nanostructured metal matrix composites publication-title: Mater Des doi: 10.1016/j.matdes.2013.04.032 – volume: 43 start-page: 2085 year: 2012 ident: 10.1016/j.jmrt.2023.05.035_bib30 article-title: Fabrication of high-strength Al/Sicp nanocomposite sheets by accumulative roll bonding publication-title: Metall Mater Trans A Phys Metall Mater Sci doi: 10.1007/s11661-011-1039-7 – volume: 54 start-page: 4921 issue: 30 year: 2020 ident: 10.1016/j.jmrt.2023.05.035_bib12 article-title: Effect of nano Al2O3 coated Ag reinforced Cu matrix nanocomposites on mechanical and tribological behavior synthesis by P/M technique publication-title: J Compos Mater doi: 10.1177/0021998320934860 – volume: 10 start-page: 277 issue: 11 year: 2022 ident: 10.1016/j.jmrt.2023.05.035_bib39 article-title: Prediction of tribological properties of alumina-coated, silver-reinforced copper nanocomposites using long short-term model combined with golden jackal optimization publication-title: Lubricants doi: 10.3390/lubricants10110277 |
| SSID | ssj0001596081 |
| Score | 2.4765098 |
| Snippet | We discovered and analyzed the new prediction model by using machine learning (ML) for the tensile strength of aluminum nanocomposites reinforced with μ-SiC... |
| SourceID | doaj crossref elsevier |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 7666 |
| SubjectTerms | Accumulative roll bonding Al– SiC nanocomposite Growth optimizer algorithm Hardness Machine learning Tensile strength |
| Title | Prediction of the tensile properties of ultrafine grained Al–SiC nanocomposites using machine learning |
| URI | https://dx.doi.org/10.1016/j.jmrt.2023.05.035 https://doaj.org/article/9694653e918245539ad381469b7fdfad |
| Volume | 24 |
| WOSCitedRecordID | wos001030065500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Open Access Full Text issn: 2238-7854 databaseCode: DOA dateStart: 20120101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: false ssIdentifier: ssj0001596081 providerName: Directory of Open Access Journals |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV25TsQwELUQoqFAnGK55IIOReSw47gEBKJAaCUObWc5PpZdLVkUFmr-gT_kS5hxsksqaGgTTxyNR5nnyfMbQo4hqzNAFRaQW4mlG2aiklse-SIuU6MhCgKb8PFG3N4Wg4Hsd1p9ISeskQduHHcqc4kSYE4CEGacZ1LbDMtWshTeem3x6xsL2dlMNeeDAZmHDqWQ_opIFJy1J2Yactf4uUYiZZoF2c7Q6-0nKwXx_k5y6iScq3Wy1iJFeta84QZZctUmWe3oB26Rp36N_1nQt3TqKWA5GgjpE0dfsMheo1oq3nmbzGrtwZAOsSWEs_Rs8vXxeTe6oJWupsgrR_IWDEYe_JA-B4qlo21PieE2ebi6vL-4jtrWCZFhSTwDV8PaCNiaeDx660rYxtlcG2Gc0ZbliZGJKUzMHRNciMwUsfdpbnNjmU9MybIdslxNK7dLKLNJYTgaas3SWANkKKy2gvsylQkveySZu06ZVlcc21tM1JxANlboboXuVjFX4O4eOVnYvDSqGr-OPscVWYxERexwAeJEtXGi_oqTHuHz9VQtuGhAAzxq9Mvke_8x-T6WA5Duk0cpPyDLs_rNHZIV8z4bvdZHIXS_AbPr8zs |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prediction+of+the+tensile+properties+of+ultrafine+grained+Al-SiC+nanocomposites+using+machine+learning&rft.jtitle=Journal+of+materials+research+and+technology&rft.au=Najjar%2C+I.M.R.&rft.au=Sadoun%2C+A.M.&rft.au=Elaziz%2C+Mohamed+Abd&rft.au=Ahmadian%2C+H.&rft.date=2023-05-01&rft.issn=2238-7854&rft.volume=24&rft.spage=7666&rft.epage=7682&rft_id=info:doi/10.1016%2Fj.jmrt.2023.05.035&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_jmrt_2023_05_035 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2238-7854&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2238-7854&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2238-7854&client=summon |