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

Autor:	Bhosale, Prashant Vishnu, Agashe, Sudhir D
Předmět:	CONVOLUTIONAL neural networks PIEZOELECTRIC materials MOBULIDAE ULTRASONICS
Zdroj:	Integrated Ferroelectrics; 2024, Vol. 240 Issue 1, p181-198, 18p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu