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Artificial neural network prediction on ultrasonic performance of bismuth-tellurite glass compositions
Journal of Materials Research and Technology ( IF 6.4 ) Pub Date : 2020-10-07 , DOI: 10.1016/j.jmrt.2020.09.107
Nuraidayani Effendy , Sidek Hj Ab Aziz , Halimah Mohamed Kamari , Mohd Hafiz Mohd Zaid , Caceja Elyca Anak Budak , Muhammad Kashfi Shabdin , Mohammad Zulhasif Ahmad Khiri , Siti Aisyah Abdul Wahab

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.



中文翻译:

铋碲玻璃组合物超声性能的人工神经网络预测

人工神经网络(ANN)是受生物神经系统启发的人工智能工具之一,具有预测眼镜的物理和弹性参数而无需熔化原材料的能力。的铋-碲酸盐玻璃与所述组合物的实验ý的Bi 2 ö 3(1- - Ý)的TeO 2,其中ÿ = 0,0.05,0.07,0.10,0.13,0.15已使用熔化和淬火方法制造。这些工作被发现,通过人工神经网络对铋-碲酸盐玻璃组合物的密度,超声速度和弹性模量的预测值与实验测量值具有非常好的一致性。图的拟合优度使用R 2值表示实验数据和预测模型之间的关系。在所有图中阐明的系数R 2值的最佳拟合为0.99942-1.0000左右,这被认为是非常令人满意的。

更新日期:2020-10-07
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