Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions
Artificial neural networks (ANN) is known as one of the artificial intelligence tools which are inspired by the biological nerve system, have a capability to predict the physical and elastic parameter of glasses without melting the raw materials. The experimental of bismuth-tellurite glasses with th...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/87931/ http://psasir.upm.edu.my/id/eprint/87931/1/ABSTRACT.pdf |
| _version_ | 1848860524844941312 |
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| author | Effendy, Nuraidayani Ab Aziz, Sidek Mohamed Kamari, Halimah Mohd Zaid, Mohd Hafiz Anak Budak, Caceja Elyca Shabdin, Muhammad Kashfi Ahmad Khiri, Mohammad Zulhasif Abdul Wahab, Siti Aisyah |
| author_facet | Effendy, Nuraidayani Ab Aziz, Sidek Mohamed Kamari, Halimah Mohd Zaid, Mohd Hafiz Anak Budak, Caceja Elyca Shabdin, Muhammad Kashfi Ahmad Khiri, Mohammad Zulhasif Abdul Wahab, Siti Aisyah |
| author_sort | Effendy, Nuraidayani |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Artificial neural networks (ANN) is known as one of the artificial intelligence tools which are inspired by the biological nerve system, have a capability to predict the physical and elastic parameter of glasses without melting the raw materials. The experimental of bismuth-tellurite glasses with the composition yBi2O3 - (1-y)TeO2 where y = 0, 0.05, 0.07, 0.10, 0.13, 0.15 have been fabricated using melting and quenching methods. These works were discovered that the prediction value by artificial neural networks for density, ultrasonic velocity, and elastic moduli of bismuth-tellurite glass composition gives a very good agreement as compared with the experimental measurements. The goodness of fit from the graph used R2 value to represent the relationship between the data presented from the experiment and prediction model. The great fit of coefficient R2 value elucidates in all figures is around 0.99942–1.0000 which is considered to be very satisfactory. |
| first_indexed | 2025-11-15T12:46:37Z |
| format | Article |
| id | upm-87931 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:46:37Z |
| publishDate | 2020 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-879312022-05-24T08:01:36Z http://psasir.upm.edu.my/id/eprint/87931/ Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions Effendy, Nuraidayani Ab Aziz, Sidek Mohamed Kamari, Halimah Mohd Zaid, Mohd Hafiz Anak Budak, Caceja Elyca Shabdin, Muhammad Kashfi Ahmad Khiri, Mohammad Zulhasif Abdul Wahab, Siti Aisyah Artificial neural networks (ANN) is known as one of the artificial intelligence tools which are inspired by the biological nerve system, have a capability to predict the physical and elastic parameter of glasses without melting the raw materials. The experimental of bismuth-tellurite glasses with the composition yBi2O3 - (1-y)TeO2 where y = 0, 0.05, 0.07, 0.10, 0.13, 0.15 have been fabricated using melting and quenching methods. These works were discovered that the prediction value by artificial neural networks for density, ultrasonic velocity, and elastic moduli of bismuth-tellurite glass composition gives a very good agreement as compared with the experimental measurements. The goodness of fit from the graph used R2 value to represent the relationship between the data presented from the experiment and prediction model. The great fit of coefficient R2 value elucidates in all figures is around 0.99942–1.0000 which is considered to be very satisfactory. Elsevier 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87931/1/ABSTRACT.pdf Effendy, Nuraidayani and Ab Aziz, Sidek and Mohamed Kamari, Halimah and Mohd Zaid, Mohd Hafiz and Anak Budak, Caceja Elyca and Shabdin, Muhammad Kashfi and Ahmad Khiri, Mohammad Zulhasif and Abdul Wahab, Siti Aisyah (2020) Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions. Journal of Materials Research and Technology-JMR&T, 9 (6). 14082 - 14092. ISSN 2238-7854; ESSN: 2214-0697 https://www.sciencedirect.com/science/article/pii/S2238785420318330 10.1016/j.jmrt.2020.09.107 |
| spellingShingle | Effendy, Nuraidayani Ab Aziz, Sidek Mohamed Kamari, Halimah Mohd Zaid, Mohd Hafiz Anak Budak, Caceja Elyca Shabdin, Muhammad Kashfi Ahmad Khiri, Mohammad Zulhasif Abdul Wahab, Siti Aisyah Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions |
| title | Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions |
| title_full | Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions |
| title_fullStr | Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions |
| title_full_unstemmed | Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions |
| title_short | Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions |
| title_sort | artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions |
| url | http://psasir.upm.edu.my/id/eprint/87931/ http://psasir.upm.edu.my/id/eprint/87931/ http://psasir.upm.edu.my/id/eprint/87931/ http://psasir.upm.edu.my/id/eprint/87931/1/ABSTRACT.pdf |