Ultrasonic and mechanical properties of binary zinc-borate glasses using artificial neural networks simulation

Abstract

The simulation-based artificial neural networks (ANN) program is one of the suitable candidates from artificial intelligence simulation which can work to predict important ultrasonic and mechanical parameters in the glass field. This research is focused on exploring the validity of this system by comparing the prediction values from ANN with the experimental measurements and other theoretical models. The ANN simulation was effectively applied to a binary zinc-borate glass system with the composition of zZnO−(100-z)B2O3 where z = 0, 40, 45, 50, 55, and 60 mol%, which was fabricated by using the melt-quenching techniques. The increase of ZnO content caused the ultrasonic velocity and elastic moduli of the glasses to exhibit a decreasing trend. The bond compression theoretical calculation compared with the experimental measurement was considered to be satisfactory with the value of the coefficient R2 being around 0.92452 to 0.98492. The Makishima-Mackenzie calculation model concerning the experimental measurement of the elastic moduli and Poisson's ratio were between 0.86628 to 0.99786. The coefficient R2 value from the ANN simulation displayed on the density, ultrasonic velocity, and elastic moduli graph is around 0.9999 to 1.0000, which is considered to be very reasonable. The values predicted by this remarkable model proved that ANN simulation is suitable for use in glass research.