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. 2024 Sep 3;17(17):4367.
doi: 10.3390/ma17174367.

Parametric Analysis of Critical Buckling in Composite Laminate Structures under Mechanical and Thermal Loads: A Finite Element and Machine Learning Approach

Omar Shabbir Ahmed  1   2 , Jaffar Syed Mohamed Ali  2 , Abdul Aabid  1 , Meftah Hrairi  2 , Norfazrina Mohd Yatim  2
Affiliations

Parametric Analysis of Critical Buckling in Composite Laminate Structures under Mechanical and Thermal Loads: A Finite Element and Machine Learning Approach

Omar Shabbir Ahmed et al. Materials (Basel). .

Abstract

This research focuses on investigating the buckling strength of thin-walled composite structures featuring various shapes of holes, laminates, and composite materials. A parametric study is conducted to optimize and identify the most suitable combination of material and structural parameters, ensuring the resilience of structure under both mechanical and thermal loads. Initially, a numerical approach employing the finite element method is used to design the C-section thin-walled composite structure. Later, various structural and material parameters like spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied. Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. Various ML techniques such as linear regression, lasso regression, decision tree, random forest, and gradient boosting are employed to assess their accuracy in comparison with finite element results. As a result, the simulation model showcases the variation in critical buckling load when altering the structural and material properties. Additionally, the machine learning models successfully predict the optimal critical buckling load under mechanical and thermal loading conditions. In summary, this paper delves into the study of the stability of C-section thin-walled composite structures with holes under mechanical and thermal loading conditions using finite element analysis and machine learning studies.

Keywords: C-section channel; FE analysis; buckling analysis; composite laminates; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
FE model (a) without holes and (b) with holes.
Figure 2
Figure 2
Description of opening and spacing ratio.
Figure 3
Figure 3
Models of the FE mesh (a) without holes and (b) with holes.
Figure 4
Figure 4
Loading conditions on the model.
Figure 5
Figure 5
Process of machine learning in the current work.
Figure 6
Figure 6
Mesh convergence study for C-section without cut-outs. Blue line is critical buckling load value and square point represents the exact value at a particular point.
Figure 7
Figure 7
Total deformation (eigenvalue buckling) in the absence of cutouts.
Figure 8
Figure 8
Total deformation (eigenvalue buckling) for circular cut-outs under mechanical load.
Figure 9
Figure 9
Total deformation (eigenvalue buckling) for circular cut-outs under thermal load.
Figure 9
Figure 9
Total deformation (eigenvalue buckling) for circular cut-outs under thermal load.
Figure 10
Figure 10
Influence of each parameter under mechanical load.
Figure 11
Figure 11
Influence of each parameter under thermal load.
Figure 12
Figure 12
Analysis of regression model under mechanical load. Pink—predicted labels and blue—actual label.
Figure 12
Figure 12
Analysis of regression model under mechanical load. Pink—predicted labels and blue—actual label.
Figure 13
Figure 13
Analysis of regression model thermal load. Pink—predicted labels and blue—actual label.
Figure 13
Figure 13
Analysis of regression model thermal load. Pink—predicted labels and blue—actual label.
See this image and copyright information in PMC

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