Concurrent multi-scale optimization of hybrid composite plates and shells for vibration

Ma, Xiangtao; Tian, Kuo; Hongqing, Li; Zhou, Yan; Hao, Peng; Wang, Bo

doi:10.1016/j.compstruct.2019.111635

Cited by 34 publications

(4 citation statements)

References 43 publications

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“…Discrete material optimization (DMO) method has been widely applied to parameterize the fiber orientations of FRP laminated structures, 38–40 which is briefly described here for the completeness of the article and more details can be consulted in References 13,35. Generally speaking, the DMO framework can be regarded as a multi‐material selection problem, in which different fiber orientations are regarded to be different candidate materials, and the unified constitutive matrix in one element or in one patch equals a weighted average of the constitutive matrix for each candidate orientation.…”

Section: Methodsmentioning

confidence: 99%

Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

Gao

et al. 2021

Numerical Meth Engineering

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This study proposes a machine learning (ML) based approach for optimizing fiber orientations of variable stiffness carbon fiber reinforced plastic (CFRP) structures, where neural networks are developed to estimate the objective function and analytical sensitivities with respect to design variables as a substitute for finite element analysis (FEA). To reduce the number of training samples and improve the regression accuracy, an active learning strategy is implemented by successively supplying effective samples along with the suboptimal process.After proper training of neural networks, a quasi-global search strategy can be applied by implementing a large number of initial designs as starting points in the optimization. In this article, a mathematical example is first presented to show the superiority of the active learning strategy. Then a benchmark design example of a CFRP plate is scrutinized to compare the proposed ML-based with the conventional FEA-based discrete material optimization (DMO) method.Finally, topology optimization of fiber orientations is performed for design of a CFRP engine hood, in which the structural performance generated from the proposed ML-based approach achieves 12.62% improvement compared with that obtained from the conventional single-initial design method. This article is anticipated to demonstrate a new alternative for design of fiber-reinforced composite structures.

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Section: Methodsmentioning

confidence: 99%

Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

Gao

et al. 2021

Numerical Meth Engineering

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“…Material quality and parameter's quantitative effect were also not reported for better and informed design knowledge. Ma et al [28] employed a discrete material optimization technique to optimize the framework for hybrid composite production. It was found that their study was only concerned with the different cost components of the materials at the expense of material quality.…”

Section: Related Work and Findingsmentioning

confidence: 99%

Cost and Quality Optimization Taguchi Design with Grey Relational Analysis of Halloysite Nanotube Hybrid Composite: CNC Machine Manufacturing

Esangbedo

Abifarin

2022

Materials

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Researchers who work on manufacturing hybrid composites have significant concerns about holistically optimizing more than one performance characteristic, as in the case of cost and quality optimization. They usually trade off one for the other. Hence, this study employed statistical tools and grey relational analyses (GRA) design to model and optimize the surface roughness and cutting force of Computer Numerical Control (CNC) machine settings to manufacture halloysite nanotube hybrid composite. In this paper, the GRA was able to address the multiple optimization complications by producing 0.6 mm depth of cut, 1500 rpm spindle speed, and 40 mmpm feed rate as the CNC machine settings for high-quality and low-cost hybrid composite. It was noticed that the mathematical and interaction modeling of surface roughness, cutting force, and grey relational grade (GRG) allowed different CNC machines to manufacture hybrid composites. This can assist researchers and production engineers of CNC machines. Variance analysis and delta statistical characteristics revealed that the depth of a cut is the most significant machine setting, with a contribution of 49.12%. This paper outlines the possible CNC machine settings for high-quality composite manufacturing. In future studies, it is recommended for researchers in the field of CNC machine manufacturing to consider the modeling analysis aspect of the optimization, which comprehensively provides the opportunity for the adjustment of CNC machines for better material performance, which has been lacking in the literature.

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“…Urbanczyk et al [9] developed a multi-physical field analysis and design optimization software COMANDO, and used the unidirectional fluid-structure coupling method to optimize the wall thickness of supersonic binary nozzle under two different constraint conditions. Ma Xiangtao et al [10] established a parallel multi-scale optimization algorithm framework, and applied it to the material distribution optimization of composite plate and shell structure, to achieve the maximum design of structural natural frequency under constraint conditions. Sun Peng et al [11] numerically simulated the structural deformation characteristics and internal/external flow characteristics of double-annular serpentine convergent nozzle under fluid-structure coupling effect, and the results show that the deformation position of serpentine nozzle after experiencing fluid-structure coupling effect is mainly distributed on the downstream wall of the first bend and the nozzle outlet, resulting in a significant reduction of nozzle aerodynamic performance.…”

Section: Introductionmentioning

confidence: 99%

Investigation on influence mechanism of length-to-diameter ratio on fluid-structure interaction characteristic of serpentine nozzle

Li,

Zhou,

Shi

et al. 2024

J. Phys.: Conf. Ser.

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The effect of different length-to-diameter ratios on the fluid-structure coupling characteristics of double serpentine nozzle for turbofan engine was studied by using serial bidirectional loose coupling method. The results show that the structural deformation of the serpentine nozzle is mainly located at the downstream channel of the first bend of the nozzle and the upper wall of the exit of the nozzle, and with the increase of the length-to-diameter ratio, the deformation amount of the nozzle gradually increases. The maximum deformation of the upper and lower walls of the serpentine nozzle occurs in the downstream region of the first bend of the serpentine nozzle. The fluid-structure coupling effect has an influence on the flow field characteristics and aerodynamic performance of the serpentine nozzle. When the length-to-diameter ratio is 2.4, the influence of fluid-structure coupling effect is lowest, and the total pressure recovery coefficient decreases by 0.40%, the flow coefficient decreases by 5.34%, and the thrust coefficient decreases by 0.92%. When the length-to-diameter ratio is 3, the influence of fluid-structure coupling effect is highest, and at this time, the total pressure recovery coefficient decreases by 0.48%, the flow coefficient decreases by 5.95%, and the thrust coefficient decreases by 1.12%.

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Concurrent multi-scale optimization of hybrid composite plates and shells for vibration

Cited by 34 publications

References 43 publications

Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

Cost and Quality Optimization Taguchi Design with Grey Relational Analysis of Halloysite Nanotube Hybrid Composite: CNC Machine Manufacturing

Investigation on influence mechanism of length-to-diameter ratio on fluid-structure interaction characteristic of serpentine nozzle

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