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Decision Tree-based Machine Learning to Optimize the Laminate Stacking of Composite Cylinders for Maximum Buckling Load and Minimum Imperfection Sensitivity

Wagner, H.N.R. and Köke, H. and Dähne, S. and Niemann, S. and Hühne, C. and Khakimova, R. (2019) Decision Tree-based Machine Learning to Optimize the Laminate Stacking of Composite Cylinders for Maximum Buckling Load and Minimum Imperfection Sensitivity. Composite Structures. Elsevier. ISSN 0263-8223 (In Press)

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Official URL: http://www.sciencedirect.com/science/article/pii/S0263822318341710

Abstract

Launch-vehicle primary structures like cylindrical shells are increasingly being built as monolithic composite and sandwich composite shells. These imperfection sensitive shells are subjected to axial compression due to the weight of the upper structural elements and tend to buckle under axial compression. In the case of composite shells the buckling load and imperfection sensitivity depend on the laminate stacking sequence. Within this paper multi-objective optimizations for the laminate stacking sequence of composite cylinder under axial compression are performed. The optimization is based on different geometric imperfection types and a brute force approach for three different ply angles. Decision tree-based machine learning is applied to derive general design recommendations which lead to maximum buckling load and a minimum imperfection sensitivity. The design recommendation are based on the relative membrane, bending, in-plane shear and twisting stiffnesses. Several optimal laminate stacking sequences are generated and compared with similar laminate configurations from literature. The results show that the design recommendations of this article lead to high-performance cylinders which outperform comparable composite shells considerably. The results of this article may be the basis for future lightweight design of sandwich and monolithic composite cylinders of modern launch-vehicle primary structures.

Item URL in elib:https://elib.dlr.de/126747/
Document Type:Article
Title:Decision Tree-based Machine Learning to Optimize the Laminate Stacking of Composite Cylinders for Maximum Buckling Load and Minimum Imperfection Sensitivity
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wagner, H.N.R.UNSPECIFIEDUNSPECIFIED
Köke, H.UNSPECIFIEDUNSPECIFIED
Dähne, S.UNSPECIFIEDUNSPECIFIED
Niemann, S.UNSPECIFIEDUNSPECIFIED
Hühne, C.christian.huehne (at) dlr.dehttps://orcid.org/0000-0002-2218-1223
Khakimova, R.UNSPECIFIEDUNSPECIFIED
Date:2019
Journal or Publication Title:Composite Structures
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:Elsevier
ISSN:0263-8223
Status:In Press
Keywords:Buckling, robust design, knockdown factor, imperfection sensitivity, composite shell, postbuckling, optimization, machine learning, decision tree
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Transport
DLR - Research area:Raumfahrt
DLR - Program:R RP - Raumtransport
DLR - Research theme (Project):R - Leitprojekt - Forschungsverbund Oberstufe
Location: Braunschweig
Institutes and Institutions:Institute of Composite Structures and Adaptive Systems > Functional Lightweight Structures
Deposited By: Hühne, Dr.-Ing. Christian
Deposited On:08 Jul 2019 15:49
Last Modified:19 Feb 2020 10:20

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