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Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions


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


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Institute of Advanced Technology
DOI Number: https://doi.org/10.1016/j.jmrt.2020.09.107
Publisher: Elsevier
Keywords: Tellurite glass; Bismuth oxide; Elastic properties; Artificial neural networks
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 24 May 2022 08:01
Last Modified: 24 May 2022 08:01
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.jmrt.2020.09.107
URI: http://psasir.upm.edu.my/id/eprint/87931
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