Optimizing frequencies of skew composite laminates with metaheuristic algorithms

Kalita, Kanak; Dey, Partha Pratim; Haldar, Salil; Gao, Xiao‐Zhi

doi:10.1007/s00366-019-00728-x

Cited by 32 publications

(11 citation statements)

References 33 publications

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“…Based on past research works on the same finite element formulation [30][31][32], it is revealed that an 18 × 18 mesh discretization of the composite plate using 9-node isoparametric elements is sufficiently accurate for the considered problems. To demonstrate the accuracy of the current FEM formulation, a validation example is included in Figure A1, Appendix A.…”

Section: Finite Element Methodsmentioning

confidence: 99%

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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: Finite Element Methodsmentioning

confidence: 99%

“…These three natural frequencies are treated here as the responses. The detailed formulation of the FEM used in this paper can be available in [30][31][32]. The FE formulation adopts a 9-node isoparametric element and is based on first-order shear deformation theory (FSDT).…”

Section: Problem 1: Low-dimensional (Ld) Problemmentioning

confidence: 99%

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

et al. 2021

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“…Initially in the CHP algorithm, a random population of “ n h ” hosts and “ n p ” parasites are created, and their fitness is measured. For this research, the number of initial host and parasite population is selected as 50 each as the recommendations of Kalita et al [ 24 ]. Each m th parasite tries to update its position at the

th iteration by taking a Lévy flight,

where

are the fitness of k th host and m th parasite at the t th iteration.…”

Section: Methodsmentioning

confidence: 99%

Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron

et al. 2020

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In this article, an improved variant of the cuckoo search (CS) algorithm named Coevolutionary Host-Parasite (CHP) is used for maximizing the metal removal rate in a turning process. The spindle speed, feed rate and depth of cut are considered as the independent parameters that describe the metal removal rate during the turning operation. A data-driven second-order polynomial regression approach is used for this purpose. The training dataset is designed using an L16 orthogonal array. The CHP algorithm is effective in quickly locating the global optima. Furthermore, CHP is seen to be sufficiently robust in the sense that it is able to identify the optima on independent reruns. The CHP predicted optimal solution presents ±10% deviations in the optimal process parameters, which shows the robustness of the optimal solution.

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“…This helps the RPSOLC algorithm to avoid getting trapped in local optima pits. The pseudocode of the current RPSOLC can be retrieved from authors' previous work [30].…”

Section: Rpsolc (Repulsive Particle Swarm Optimization With Local Sea...mentioning

confidence: 99%

Optimization of Plasma Enhanced Chemical Vapor Deposition Process Parameters for Hardness improvement of Diamond Like Carbon Coatings

Kalita¹,

Ghadai²

2022

Scientia Iranica

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Recently there has been a surge in the usage of metaheuristic algorithms to design materials with optimum performance. In this article, one such recently proposed metaheuristic algorithm called RPSOLC (Repulsive Particle Swarm Optimization with Local search and Chaotic perturbation) has been used to design diamond-like carbon (DLC) thin films having better hardness. Based on a Box-Behnken design, 15 independent experiments on DLC deposition are conducted in a PECVD (plasma enhanced chemical vapor deposition) setup by varying the CH 4 -Argon flow rate, hydrogen flow rate and the deposition temperature. The nano-hardness of the DLCs are evaluated using nano-indention tests. The hardness is thenexpressed as the function of the three process parameters using a polynomial regression metamodel. Finally, the metamodel is optimized using RPSOLC and compared with optimal predictions of a traditional GA. It is seen that RPSOLC has faster convergence and is more reliable than the GA. In general, a high H 2 flow rate along with low CH 4 -Ar flow rate and high temperature is found to be beneficial in improving the hardness.

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Optimizing frequencies of skew composite laminates with metaheuristic algorithms

Cited by 32 publications

References 33 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

Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron

Optimization of Plasma Enhanced Chemical Vapor Deposition Process Parameters for Hardness improvement of Diamond Like Carbon Coatings

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