1. Linear refractive index and density prediction of transparent B2O3-CaO-Li2O glasses reinforced with Sb2O3 utilizing machine learning techniques.
- Author
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Al-Ghamdi, Hanan, Alsaif, Norah A. M., Ahmmad, Shaik Kareem, Ahmed, M. M., Shams, M. S., El-Refaey, Adel M., Abdelghany, A. M., Shaaban, Shaaban M., Rammah, Y. S., and Elsad, R. A.
- Subjects
ARTIFICIAL neural networks ,MACHINE learning ,TRANSPARENT ceramics ,RANDOM forest algorithms ,GLASS - Abstract
In the present study, for the first time the machine learning (ML) based refractive index (n) approach is established depends on the density (ρ) parameter of glasses for a dataset of 2000 oxide glasses to predict refractive index of B
2 O3 -CaO-Li2 O-Sb2 O3 glasses. Density of the investigated glasses varied from 2.56 to 2.97 gm/cm3 . The corresponding refractive index was changed from 2.540 to 2.405. The refractive index prediction based on density parameter derived from the density of glasses and constant 'K'. For all M-L techniques including gradient descent (GD), artificial neural network (ANN), and random forest regression (RFR), the density factor is used as an independent variable and the experimental refractive index as a dependent variable. The data set of 10,000 oxide glass samples was employed to forecast density using a variety of machine learning approaches. In comparison to other models, the Random forest regression (RFR) model fitted the glass data with the highest R2 value of 0.949 for refractive index prediction and 0.925 for density prediction. For both the prediction of density and refractive index, the R2 is controlled to 0.932 and 0.9223, respectively. The highest R2 values for refractive index and density prediction were gained when the tanh activation function was used in an artificial neural network (ANN) with varied activation functions. [ABSTRACT FROM AUTHOR]- Published
- 2024
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