Damage Identification in CFRP Laminates Using Neural Network and Experimental Data

Autor:	Yasukazu Nishi, Goichi, Yumi Fujikawa, Issei
Rok vydání:	1996
Předmět:	Materials science Artificial neural network business.industry Mechanical Engineering Delamination Experimental data Structural engineering Laminated beam Test method Identification (information) Mechanics of Materials Transverse cracking General Materials Science business
Zdroj:	TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series A. 62:2338-2343
ISSN:	1884-8338 0387-5008
DOI:	10.1299/kikaia.62.2338
Popis:	To enable the wider use of CFRP, damage especially in the laminated direction such as transverse cracking, delamination or fiber-matrix debonding should be easily and economically detected and a reasonable test method for detecting damage nondestructively should also be devised. We present the application of a hierachical neural network to damage identification in a CFRP laminated beam and discuss the accuracy and the efficiency of this method. It is found that the neural network is a useful and practical nondestructive method for the first stage approximation of damage identification in the CFRP laminated beam. Although the network is developed through an iterative calculation, this network is suited for field measurement because the damage can be identified by simple operations and multiplications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c44ff15c7c6f2ccd4529251440773070 https://doi.org/10.1299/kikaia.62.2338 Zobrazit plný text záznamu