Damage Detection and Prediction of Ultrasonic Behaviours in Piezoelectric Materials Using Hybrid Deep Convolutional Neural Network

Abstract

In this work the material is first checked for its health by using point contact method. Then the behavioural characteristic of the material is found once the material is decided as healthy one. After, this the healthy material is drilled in microns to produce damage. The damaged materials are studied by point contact method and their deflections are illustrated. The behaviour of the piezoelectric material is also predicted by using hybrid Deep Convolution Neural Network based on Manta Ray Foraging Optimisation (Hybrid DCNN-MRFO). This work has attained for about 93% in accuracy.

Keywords:

• Piezoelectric material
• graphene and Aluminium Nitrate composite materials coupled with a piezoelectric Acryl sheet
• Point contact method
• Hybrid DCNN-MRFO

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Disclosure statement

No potential conflict of interest was reported by the authors.