A maximum entropy supervised learning algorithm for the identification of skin/core debonding in honeycomb aluminium panels

• Post by: alejandro
• April 11, 2020
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The Twelfth International Conference on Computational Structures Technology (CST2014)
2 – 5 September 2014, Naples, Italy
Proceedings of CST2014
Civil-Comp Press, Stirlingshire, UK, Paper 120, 2014

V. Meruane, V. del Fierro and A. Ortiz-Bernardin

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 paper presents a new supervised learning algorithm to identify debonded regions in aluminium 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 modelled with finite elements using a simplified three-panel shell model. The adhesive layer between the skin and core is modelled using linear springs, the rigidities of which are reduced in debonded sectors. The algorithm is validated using experimental data of an aluminium honeycomb panel under different damage scenarios.

Keywords: sandwich structures, debonding, honeycomb, damage assessment, maximum entropy

Original Journal Article:  https://doi.org/10.4203/ccp.106.120

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NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.