The elastic, mechanical and optical properties of bismuth modified borate glass: Experimental and artificial neural network simulation

The introduction of artificial neural networks (ANNs) in the glass field has greatly improved this industry to further increase fabrication productivity. ANNs are the systems that help the glass expert to estimate a few parameters such as density, molar volume, ultrasonic velocity, elastic moduli and optical band gap in the glass composition. The greatness of this system was implemented in a series of bismuth-borate (Bi2O3-B2O3) glasses which have been successfully produced using melting and quenching methods with the configuration of mBi(2)O(3)-(100-m)B2O3 where m = 0, 40, 45, 50, 55, 60 mol%. In this present works, the experimental values resulting from the composition of this glass series were compared with the values obtained from the estimation by ANNs. This study has concluded that the ANNs system is relevant to be used in the fields of glass industry since the coefficient of R-2 values showed by the density, molar volume, ultrasonic velocity, elastic moduli and optical band gap graph is between 0.998 and 1.0000 which believed highly desirable.

Automated summary

The introduction of artificial neural networks has significantly enhanced fabrication productivity in the glass industry by accurately estimating various glass parameters. The comparison between experimental values and ANN estimations confirms the relevance of ANNs in the glass industry. The high R-2 values obtained from density, molar volume, ultrasonic velocity, elastic moduli, and optical band gap graphs further support the efficiency and desirability of using the ANNs system in the glass field.