A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates
Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, due to manufacturing defects or impact loads, these structures can be subject to imperfect bonding or debonding between the skin and the honeycomb core. The presence of debonding reduces the bending stiffness of...
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doaj-230c0a96df2a4aa49f3aea5c16c524b32020-11-24T23:31:18ZengMDPI AGEntropy1099-43002014-05-011652869288910.3390/e16052869e16052869A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum PlatesViviana Meruane0Valentina del Fierro1Alejandro Ortiz-Bernardin2Department of Mechanical Engineering, Universidad de Chile, Beauchef 850, Santiago, ChileDepartment of Mechanical Engineering, Universidad de Chile, Beauchef 850, Santiago, ChileDepartment of Mechanical Engineering, Universidad de Chile, Beauchef 850, Santiago, ChileHoneycomb sandwich structures are used in a wide variety of applications. Nevertheless, due to manufacturing defects or impact loads, these structures can be subject to imperfect bonding or debonding between the skin and the honeycomb core. The presence of debonding reduces the bending stiffness of the composite panel, which causes detectable changes in its vibration characteristics. This article presents a new supervised learning algorithm to identify debonded regions in aluminum honeycomb panels. The algorithm uses a linear approximation method handled by a statistical inference model based on the maximum-entropy principle. The merits of this new approach are twofold: training is avoided and data is processed in a period of time that is comparable to the one of neural networks. The honeycomb panels are modeled with finite elements using a simplified three-layer shell model. The adhesive layer between the skin and core is modeled using linear springs, the rigidities of which are reduced in debonded sectors. The algorithm is validated using experimental data of an aluminum honeycomb panel under different damage scenarios.http://www.mdpi.com/1099-4300/16/5/2869Sandwich structuresdebondinghoneycombdamage assessmentmaximum-entropy principlelinear approximation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Viviana Meruane Valentina del Fierro Alejandro Ortiz-Bernardin |
spellingShingle |
Viviana Meruane Valentina del Fierro Alejandro Ortiz-Bernardin A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates Entropy Sandwich structures debonding honeycomb damage assessment maximum-entropy principle linear approximation |
author_facet |
Viviana Meruane Valentina del Fierro Alejandro Ortiz-Bernardin |
author_sort |
Viviana Meruane |
title |
A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates |
title_short |
A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates |
title_full |
A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates |
title_fullStr |
A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates |
title_full_unstemmed |
A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates |
title_sort |
maximum entropy approach to assess debonding in honeycomb aluminum plates |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2014-05-01 |
description |
Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, due to manufacturing defects or impact loads, these structures can be subject to imperfect bonding or debonding between the skin and the honeycomb core. The presence of debonding reduces the bending stiffness of the composite panel, which causes detectable changes in its vibration characteristics. This article presents a new supervised learning algorithm to identify debonded regions in aluminum honeycomb panels. The algorithm uses a linear approximation method handled by a statistical inference model based on the maximum-entropy principle. The merits of this new approach are twofold: training is avoided and data is processed in a period of time that is comparable to the one of neural networks. The honeycomb panels are modeled with finite elements using a simplified three-layer shell model. The adhesive layer between the skin and core is modeled using linear springs, the rigidities of which are reduced in debonded sectors. The algorithm is validated using experimental data of an aluminum honeycomb panel under different damage scenarios. |
topic |
Sandwich structures debonding honeycomb damage assessment maximum-entropy principle linear approximation |
url |
http://www.mdpi.com/1099-4300/16/5/2869 |
work_keys_str_mv |
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