Optimal design of thin-walled functionally graded beams for buckling problems

Nguyen, Tan-Tien; Lee, Jae Hong

doi:10.1016/j.compstruct.2017.07.024

Cited by 21 publications

(12 citation statements)

References 49 publications

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“…However, these structures show susceptibility to local buckling and buckling failure [ 1 , 2 ]. Buckling analysis and the post-buckling response of such weight-optimized structures have been the topic of many research papers, such as [ 3 , 4 , 5 , 6 ], especially in the field of composite materials [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Bimetallic Thin-Walled Box Beam Thermal Buckling Response

et al. 2022

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A beam model for thermal buckling analysis of a bimetallic box beam is presented. The Euler–Bernoulli–Vlasov beam theory is employed considering large rotations but small strains. The nonlinear stability analysis is performed using an updated Lagrangian formulation. In order to account for the thermal effects of temperature-dependent (TD) and temperature-independent (TID) materials, a uniform temperature rise through beam wall thickness is considered. The numerical results for thin-walled box beams are presented to investigate the effects of different boundary conditions, beam lengths and material thickness ratios on the critical buckling temperature and post-buckling responses. The effectiveness and accuracy of the proposed model are verified by means of comparison with a shell model. It is revealed that all of the abovementioned effects are invaluable for buckling analysis of thin-walled beams under thermal load. Moreover, it is shown that the TD solutions give lower values than the TID one, emphasizing the importance of TD materials in beams.

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Section: Introductionmentioning

confidence: 99%

Bimetallic Thin-Walled Box Beam Thermal Buckling Response

et al. 2022

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“…The functionally graded materials (FGMs) with superior metal and ceramic continuity which aid in the elimination of abrupt transitions between distinct materials have been introduced. Thermal and flexural resistances are among their advantages, and thin-walled structures integrated with FGMs have been developed in many studies [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

Nguyen

et al. 2021

STCE

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We study static mechanical behavior of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using a non-traditional computational approach based on artificial neural network. One of the main objectives of our approach is to save the computational cost for the optimization process, which is usually time-consuming by using traditional methods such as finite element method (FEM). In this study, 1000 data sets randomly generated by the FEM through iterations are used for the training process to get optimal weights. Based on these obtained optimal weights, beam behaviors under the changes in material distribution through thickness could then be predicted. In this model, the ANN's inputs are the gradation index of the power-law distribution and thickness, while the outputs are compliance and beam displacements. The computed results are verified against those derived from the FEM.

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“…The authors also found that the stress distribution, critical buckling load, natural frequency, and weight are among the most popular objective functions. Many researchers take advantage of the simplicity of implementation and use the Power-law index as the design variable (Na and Kim 2009;Nguyen and Lee 2017;Khorsand and Tang 2018;Moita et al 2017;Franco et al 2018). On a side note, the Power-law function can also be used to describe features of the structure other than the material.…”

Section: Introductionmentioning

confidence: 99%

“…It is also quite frequent the use of B-splines (Taheri et al 2014;Kim et al 2018;Lieu et al 2018;Lieu and Lee 2019a;Lieu and Lee 2019b;Wang et al 2019;Do et al 2018;Ribeiro et al 2020) and piecewise cubic interpolation (Vel and Pelletier 2006;Ashjari and Khoshravan 2014;Asgari 2016;Nguyen and Lee 2017) to describe the volume fraction distribution. Both models allow more design flexibility than simple closed-form expressions.…”

Section: Introductionmentioning

confidence: 99%

Kriging-based optimization of functionally graded structures

Maia

Parente

Melo

2021

Struct Multidisc Optim

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This work presents an efficient methodology for the optimum design of functionally graded structures using a Krigingbased approach. The method combines an adaptive Kriging framework with a hybrid particle swarm optimization (PSO) algorithm to improve the computational efficiency of the optimization process. In this approach, the surrogate model is used to replace the high-fidelity structural responses obtained by a NURBS-based isogeometric analysis. In addition, the impact of key factors on surrogate modelling, as the correlation function, the infill criterion used to update the surrogate model, and the constraint handling is assessed for accuracy, efficiency, and robustness. The design variables are related to the volume fraction distribution and the thickness. Displacement, fundamental frequency, buckling load, mass, and ceramic volume fraction are used as objective functions or constraints. The effectiveness and accuracy of the proposed algorithm are illustrated through a set of numerical examples. Results show a significant reduction in the computational effort over the conventional approach.

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Optimal design of thin-walled functionally graded beams for buckling problems

Cited by 21 publications

References 49 publications

Bimetallic Thin-Walled Box Beam Thermal Buckling Response

Bimetallic Thin-Walled Box Beam Thermal Buckling Response

Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

Kriging-based optimization of functionally graded structures

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