Multiobjective optimization of laminated composite parts with curvilinear fibers using Kriging-based approaches

Passos, Adriano Gonçalves dos; Luersen, Marco Antônio

doi:10.1007/s00158-017-1800-7

Cited by 26 publications

(3 citation statements)

References 36 publications

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“…Several researchers have already adopted various metamodeling algorithms ranging from polynomial regression (PR) [1,2] to genetic programming (GP) [3] to the artificial neural network (ANN) for estimation of static and dynamic behaviors of laminated composite structures. Applications of the metamodels in laminated structures have also varied from prediction [4,5] to uncertainty quantification [6,7] to single-objective [8,9] and multiobjective optimization [10,11]. Kalita et al [1] performed a comprehensive study on polynomial regression (PR) metamodels for dynamic analysis of laminated plates.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

et al. 2021

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High-fidelity structural analysis using numerical techniques, such as finite element method (FEM), has become an essential step in design of laminated composite structures. Despite its high accuracy, the computational intensiveness of FEM is its serious drawback. Once trained properly, the metamodels developed with even a small training set of FEM data can be employed to replace the original FEM model. In this paper, an attempt is put forward to investigate the utility of radial basis function (RBF) metamodels in the predictive modelling of laminated composites. The effectiveness of various RBF basis functions is assessed. The role of problem dimensionality on the RBF metamodels is studied while considering a low-dimensional (2-variable) and a high-dimensional (16-variable) problem. The effect of uniformity of training sample points on the performance of RBF metamodels is also explored while considering three different sampling methods, i.e., random sampling, Latin hypercube sampling and Hammersley sampling. It is observed that relying only on the performance metrics, such as cross-validation error that essentially reuses training samples to assess the performance of the metamodels, may lead to ill-informed decisions. The performance of metamodels should also be assessed based on independent test data. It is further revealed that uniformity in training samples would lead towards better trained metamodels.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

et al. 2021

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“…Liu et al (2020), for example, adopted a multi-phase sampling strategy to update the surrogate model, while Zhang et al (2020) opted for a fixed surrogate model. Although Krigingbased approaches have been extensively applied to assist engineering design optimization problems (Cheng et al 2014;Gan and Gu 2018;Xing et al 2020;Chunna et al 2020), including problems regarding laminated composite structures (Zhu et al 2012;Passos and Luersen 2017;Keshtegar et al 2020), to the best of the authors' knowledge, the Kriging potential has not yet been explored for optimizing plates and shells made of FGM.…”

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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“…The package, named moko (acronym for Multi-Objective Kriging Optimization), is written in R language and presents three multi-objective optimization algorithms: (i) MEGO, in which the efficient global optimization algorithm (EGO) is applied to a single-objective function consisting of a weighted combination of the objectives, (ii) HEGO, which involves sequential maximization of the expected hypervolume improvement, and (iii) MVPF, an approach based on sequential minimization of the variance of the predicted Pareto front. The first two are implementations of well-known approaches of the literature, and MVPF is a novel technique first presented in Passos and Luersen (2016) and in Passos and Luersen (2018), in which a composite panel with curved fibers is optimized for multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization with Kriging surrogates using “moko”, an open source package

Passos

Luersen

2018

Lat. Am. j. solids struct.

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Many modern real-world designs rely on the optimization of multiple competing goals. For example, most components designed for the aerospace industry must meet some conflicting expectations. In such applications, low weight, low cost, high reliability, and easy manufacturability are desirable. In some cases, bounds for these requirements are not clear, and performing mono-objective optimizations might not provide a good landscape of the required optimal design choices. For these cases, finding a set of Pareto optimal designs might give the designer a comprehensive set of options from which to choose the best design. This article shows the main features and functionalities of an open source package, developed by the present authors, to solve constrained multi-objective problems. The package, named moko (acronym for Multi-Objective Kriging Optimization), was built under the open source programming language R. Popular Kriging based multiobjective optimization strategies, as the expected volume improvement and the weighted expected improvement, are available in the package. In addition, an approach proposed by the authors, based on the exploration using a predicted Pareto front is implemented. The latter approach showed to be more efficient than the two other techniques in some case studies performed by the authors with moko.

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Multiobjective optimization of laminated composite parts with curvilinear fibers using Kriging-based approaches

Cited by 26 publications

References 36 publications

Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites

Kriging-based optimization of functionally graded structures

Multi-objective optimization with Kriging surrogates using “moko”, an open source package

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