a maximum entropy approach to assess debonding in honeycomb aluminum plates

Clicks: 131
ID: 211710
2014
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #344 of 406 articles by views in European journal of medicinal chemistry

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 406 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
meruane2014entropya Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Viviana Meruane;Valentina del Fierro;Alejandro Ortiz-Bernardin
Journal European journal of medicinal chemistry
Year 2014
DOI
10.3390/e16052869
URL
http://www.mdpi.com/1099-4300/16/5/2869
Keywords
damage assessment sandwich structures

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.