Predicting the mechanical properties of Cu-Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments; Ceramics International; Vol. 48, iss. 6
| Parent link: | Ceramics International Vol. 48, iss. 6.— 2022.— [P. 24439-24453] |
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| Müşterek Yazar: | |
| Diğer Yazarlar: | , , , , |
| Özet: | Title screen Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu-Al2O3 nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu-Al2O3 nanocomposites with different Al2O3 concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu-Al2O3 nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength. Режим доступа: по договору с организацией-держателем ресурса |
| Dil: | İngilizce |
| Baskı/Yayın Bilgisi: |
2022
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| Konular: | |
| Online Erişim: | https://doi.org/10.1016/j.ceramint.2021.11.322 |
| Materyal Türü: | MixedMaterials Elektronik Kitap Bölümü |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668695 |
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| 200 | 1 | |a Predicting the mechanical properties of Cu-Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments |f I. M. R. Najjar, A. M. Sadou, Ch. S. Alsoruji [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 57 tit.] | ||
| 330 | |a Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu-Al2O3 nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu-Al2O3 nanocomposites with different Al2O3 concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu-Al2O3 nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Ceramics International | ||
| 463 | |t Vol. 48, iss. 6 |v [P. 24439-24453] |d 2022 | ||
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| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a metal matrix nanocomposites | |
| 610 | 1 | |a microindentation | |
| 610 | 1 | |a artificial neural network | |
| 610 | 1 | |a red fox optimization | |
| 610 | 1 | |a random vector functional link | |
| 610 | 1 | |a нанокомпозиты | |
| 610 | 1 | |a металлическая матрица | |
| 610 | 1 | |a микровдавливание | |
| 610 | 1 | |a искусственные нейронные сети | |
| 701 | 1 | |a Najjar |b I. M. R. | |
| 701 | 1 | |a Sadou |b A. M. | |
| 701 | 1 | |a Alsoruji |b Ch. S. |g Ghazi | |
| 701 | 1 | |a Mokhamed Elsaed (Mohamed Abd Elaziz) |b A. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1987- |g Akhmed Mokhamed |3 (RuTPU)RU\TPU\pers\46943 | |
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