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Damage detection and identification ...

Cecchini, Andres.

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Damage detection and identification in sandwich composites using neural networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Damage detection and identification in sandwich composites using neural networks./
作者:	Cecchini, Andres.
面頁冊數:	152 p.
附註:	Source: Masters Abstracts International, Volume: 43-06, page: 2388.
Contained By:	Masters Abstracts International43-06.
標題:	Engineering, Mechanical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1427037
ISBN:	9780542136993

Damage detection and identification in sandwich composites using neural networks.
Cecchini, Andres.

Damage detection and identification in sandwich composites using neural networks. - 152 p.

Source: Masters Abstracts International, Volume: 43-06, page: 2388.

Thesis (M.S.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2005.

Marine, aerospace, ground and civil structures can receive unexpected loading that may compromise integrity during their life span. Therefore, improvement in detecting damage can save revenue and lives depending upon the application. The prognostic capability is usually a function of the examiner's experience, background and data collection during the evaluation. Nondestructive evaluation (NDE) methods are varied and specific to a given type of system (material, damage type, loading and environmental scenarios). As a result, one method of damage detection alone cannot examine all possible conditions and may even give false readings. In other words, by using more than one NDE technique, the probability of ensuring a more accurate detection increases. This work examined various existing NDE techniques to assess damage in sandwich composites structures including: vibration modal analysis, transient thermal response, and acoustic emission. Sandwich composites consisting of two carbon fiber/epoxy matrix face sheets laminated onto a urethane foam core were experimentally and analytically characterized using vibration, and thermal response to detect the presence of various types of damages. A neural network (NN) approach that uses vibration and thermal signatures to determine the condition of a composite sandwich structure is purposed. The data used to train a probabilistic neural network (PNN) were provided by numerical simulations. Literature offers substantial evidence of the validity of each of the chosen damage detection schemes separately. However, we will show that these methods can work jointly to complement each other in detecting the state of a sandwich composite structure.

ISBN: 9780542136993Subjects--Topical Terms:

783786
Engineering, Mechanical.

Damage detection and identification in sandwich composites using neural networks.
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502 $a Thesis (M.S.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2005.
520 $a Marine, aerospace, ground and civil structures can receive unexpected loading that may compromise integrity during their life span. Therefore, improvement in detecting damage can save revenue and lives depending upon the application. The prognostic capability is usually a function of the examiner's experience, background and data collection during the evaluation. Nondestructive evaluation (NDE) methods are varied and specific to a given type of system (material, damage type, loading and environmental scenarios). As a result, one method of damage detection alone cannot examine all possible conditions and may even give false readings. In other words, by using more than one NDE technique, the probability of ensuring a more accurate detection increases. This work examined various existing NDE techniques to assess damage in sandwich composites structures including: vibration modal analysis, transient thermal response, and acoustic emission. Sandwich composites consisting of two carbon fiber/epoxy matrix face sheets laminated onto a urethane foam core were experimentally and analytically characterized using vibration, and thermal response to detect the presence of various types of damages. A neural network (NN) approach that uses vibration and thermal signatures to determine the condition of a composite sandwich structure is purposed. The data used to train a probabilistic neural network (PNN) were provided by numerical simulations. Literature offers substantial evidence of the validity of each of the chosen damage detection schemes separately. However, we will show that these methods can work jointly to complement each other in detecting the state of a sandwich composite structure.
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