Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force

The paper presents the optimisation of thin-walled composite structures on a representative sample of a thin-walled column made of carbon laminate with a channel section-type profile. The optimisation consisted of determining the configuration of laminate layers for which the tested structure has th...

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Main Authors: Karol Szklarek, Jakub Gajewski
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Materials
Subjects:
FEM
ANN
Online Access:https://www.mdpi.com/1996-1944/13/17/3881
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spelling doaj-453c6495e8914c10b25244ba1888c8182020-11-25T03:16:38ZengMDPI AGMaterials1996-19442020-09-01133881388110.3390/ma13173881Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling ForceKarol Szklarek0Jakub Gajewski1Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, PolandDepartment of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, PolandThe paper presents the optimisation of thin-walled composite structures on a representative sample of a thin-walled column made of carbon laminate with a channel section-type profile. The optimisation consisted of determining the configuration of laminate layers for which the tested structure has the greatest resistance to the loss of stability. The optimisation of the layer configuration was performed using two methods. The first method, divided into two stages to reduce the time, was to determine the optimum arrangement angle in each laminate layer using finite element methods (FEM). The second method employed artificial neural networks for predicting critical buckling force values and the creation of an optimisation tool. Artificial neural networks were combined into groups of networks, thereby improving the quality of the obtained results and simplifying the obtained neural networks. The results from computations were verified against the results obtained from the experiment. The optimisation was performed using ABAQUS<sup>®</sup> and STATISTICA<sup>®</sup> software.https://www.mdpi.com/1996-1944/13/17/3881laminatesply orientationoptimisationFEMANNartificial neural network
collection DOAJ
language English
format Article
sources DOAJ
author Karol Szklarek
Jakub Gajewski
spellingShingle Karol Szklarek
Jakub Gajewski
Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force
Materials
laminates
ply orientation
optimisation
FEM
ANN
artificial neural network
author_facet Karol Szklarek
Jakub Gajewski
author_sort Karol Szklarek
title Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force
title_short Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force
title_full Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force
title_fullStr Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force
title_full_unstemmed Optimisation of the Thin-Walled Composite Structures in Terms of Critical Buckling Force
title_sort optimisation of the thin-walled composite structures in terms of critical buckling force
publisher MDPI AG
series Materials
issn 1996-1944
publishDate 2020-09-01
description The paper presents the optimisation of thin-walled composite structures on a representative sample of a thin-walled column made of carbon laminate with a channel section-type profile. The optimisation consisted of determining the configuration of laminate layers for which the tested structure has the greatest resistance to the loss of stability. The optimisation of the layer configuration was performed using two methods. The first method, divided into two stages to reduce the time, was to determine the optimum arrangement angle in each laminate layer using finite element methods (FEM). The second method employed artificial neural networks for predicting critical buckling force values and the creation of an optimisation tool. Artificial neural networks were combined into groups of networks, thereby improving the quality of the obtained results and simplifying the obtained neural networks. The results from computations were verified against the results obtained from the experiment. The optimisation was performed using ABAQUS<sup>®</sup> and STATISTICA<sup>®</sup> software.
topic laminates
ply orientation
optimisation
FEM
ANN
artificial neural network
url https://www.mdpi.com/1996-1944/13/17/3881
work_keys_str_mv AT karolszklarek optimisationofthethinwalledcompositestructuresintermsofcriticalbucklingforce
AT jakubgajewski optimisationofthethinwalledcompositestructuresintermsofcriticalbucklingforce
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