Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method

Materials

Giuseppe Lamanna¹  Francesco Caputo¹  Donato Perfetto¹  Alessandro De Luca¹  Marco Perfetto¹ 

[1] Department of Engineering, University of Campania “L. Vanvitelli”, Via Roma 29, 81031 Aversa, Italy;

关键词: Artificial Neural Network (ANN);    guided waves;    Structural Health Monitoring (SHM);    Finite Element Analysis (FEA);    damage detection;    metals;   

DOI  :  10.3390/ma14247602

来源: DOAJ

【 摘 要 】

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.

【 授权许可】

Unknown