Prediction of ultrasonic velocities in ternary oxide glasses using microstructural properties of the constituents as predictor variables; Artificial Neural Network (ANN) approach

The longitudinal and shear velocities of ultrasonic waves in glass systems are influenced bythe microstructural properties and compositions of the chemical constituents. the relationship betweenthem is highly non-linear and very complex. artificial neural networks (ann) are adaptive and parallelinformation processing systems that have the potential to learn by examples and capture the non-linearas well as complex relationships between its inputs and outputs. neural networks are invaluable whereformal analysis would be difficult or impossible. an attempt has been made to predict the ultrasonicvelocities in tricomponent oxide glass systems, using the microstructural properties of the constituentsas inputs to the ann.