Computational Technology Resources - CCP

V. Meruane, V. del Fierro, A. Ortiz-Bernardin, "A Maximum Entropy Supervised Learning Algorithm for the Identification of Skin/Core Debonding in Honeycomb Aluminium Panels", in , (Editors), "Proceedings of the Twelfth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 120, 2014. doi:10.4203/ccp.106.120

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

Summary

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.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £65 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 106 PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: Paper 120 A Maximum Entropy Supervised Learning Algorithm for the Identification of Skin/Core Debonding in Honeycomb Aluminium Panels V. Meruane, V. del Fierro and A. Ortiz-Bernardin Department of Mechanical Engineering, Universidad de Chile, Santiago, Chile doi:10.4203/ccp.106.120 purchase the full-text of this paper Full Bibliographic Reference for this paper V. Meruane, V. del Fierro, A. Ortiz-Bernardin, "A Maximum Entropy Supervised Learning Algorithm for the Identification of Skin/Core Debonding in Honeycomb Aluminium Panels", in , (Editors), "Proceedings of the Twelfth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 120, 2014. doi:10.4203/ccp.106.120 Keywords: sandwich structures, debonding, honeycomb, damage assessment, maximum entropy.. Summary 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. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £65 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions