Optimization of composite stiffened panels under mechanical and hygrothermal loads using neural networks and genetic algorithms

Marín, L.; Trias, D.; Badalló, Pere; Rus, Guillermo; Mayugo, J.A.

doi:10.1016/j.compstruct.2012.04.024

Cited by 93 publications

(27 citation statements)

References 27 publications

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“…To release the computational burden caused by the large number of iterations, surrogate models have been successfully utilized in the optimization of stiffened panels [26][27][28].…”

Section: Surrogate-based Optimization Technologymentioning

confidence: 99%

Generatrix shape optimization of stiffened shells for low imperfection sensitivity

Wang

Hao

et al. 2014

Sci. China Technol. Sci.

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According to previous studies, stiffened shells with convex hyperbolic generatrix shape are less sensitive to imperfections. In this study, the effects of generatrix shape on the performances of elastic and plastic buckling in stiffened shells are investigated. Then, a more general description of generatrix shape is proposed, which can simply be expressed as a convex B-spline curve (controlled by four key points). An optimization framework of stiffened shells with a convex B-spline generatrix is established, with optimization objective being measured in terms of nominal collapse load, which can be expressed as a weighted sum of geometrically imperfect shells. The effectiveness of the proposed framework is demonstrated by a detailed comparison of the optimum designs for the B-spline and hyperbolic generatrix shapes. The decrease of imperfection sensitivity allows for a significant weight saving, which is particularly important in the development of future heavy-lift launch vehicles. stiffened shell, collapse, imperfection sensitivity, generatrix shape, optimization Citation:Wang B, Hao P, Li G, et al. Generatrix shape optimization of stiffened shells for low imperfection sensitivity.

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“…To release the computational burden caused by the large number of iterations, surrogate models have been successfully utilized in the optimization of stiffened panels [26][27][28].…”

Section: Surrogate-based Optimization Technologymentioning

confidence: 99%

Generatrix shape optimization of stiffened shells for low imperfection sensitivity

Wang

Hao

et al. 2014

Sci. China Technol. Sci.

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“…As discussed in [20,21], hybrid intelligent systems based on different integration schemes of neural networks, fuzzy logic and genetic algorithms have recently received much attention. Artificial neural networks (ANNs) and genetic algorithms (GA) have proved to be very effective in substitution of direct simulation and optimization in several contests, including manufacturing processes [22][23][24][25][26][27], composite material performance [28,29], mechanical and/or microstructural properties [30], and assisted process planning [31,32]. As far as the application of ANNs to curing simulation and optimization is concerned, early contributions can be individuated in [33,34], discussing the development of a static neural network to simulate the curing process, recalled by a nonlinear programming scheme.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

2016

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An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures

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“…A case of special interest reported in the scientific literature is the optimization of the stacking sequence of composite laminates, for which GA have been used successfully [12,13]. However, in situations where the stacking sequence cannot be considered as a design variable but a imposed requirement, the minimization of the weight is achieved with geometrical parameters [14,15]. In that case, what makes different the optimization of composite structures from other materials is the use of failure mode based failure criteria such as Puck's [16] and LaRC [17].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads

Badalló

Trias

Marín

et al. 2013

Composites Part B: Engineering

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Please cite this article as: Badalló, P., Trias, D., Marín, L., Mayugo, J.A., A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads, Composites: Part B (2012), doi: http://dx.doi.org/10. 1016/j.compositesb.2012.10.037 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads AbstractOptimization methods are close to become a common task in the design process of many mechanical engineering fields, specially those related with the use of composite materials which offer the flexibility in the design of both the shape and the material properties and so, are very suitable to any optimization process. While nowadays there exist a large number of solution methods for optimization problems there is not much information about which method may be most reliable for a specific problem. Genetic Algorithms have been presented as a family of methods which can handle most of engineering problems. However, starting from a common basic set of rules many algorithms which differ slightly from each other have been implemented even in commercial software packages. This work presents a comparative study of three common Genetic Algorithms:Archive-based Micro Genetic Algorithm (AMGA), Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) considering three different strategies for the initial population. Their performance in terms of solution, computational time and number of generations was compared. The benchmark problem was the optimization of a Tshaped stringer commonly used in CFRP stiffened panels. The objectives of the optimization were to minimize the mass and to maximize the critical buckling load. The comparative study reveals that NSGA-II and AMGA seem the most suitable algorithms for this kind of problem.

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Optimization of composite stiffened panels under mechanical and hygrothermal loads using neural networks and genetic algorithms

Cited by 93 publications

References 27 publications

Generatrix shape optimization of stiffened shells for low imperfection sensitivity

Generatrix shape optimization of stiffened shells for low imperfection sensitivity

Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

A comparative study of genetic algorithms for the multi-objective optimization of composite stringers under compression loads

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