Discrete topology optimization of ply orientation for a carbon fiber reinforced plastic (CFRP) laminate vehicle door

Wu, Chi; Gao, Yunkai; Fang, Jianguang; Lund, Erik; Li, Qing

doi:10.1016/j.matdes.2017.04.089

Cited by 73 publications

(23 citation statements)

References 28 publications

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“…The objective function and the sensitivities with respect to design variables are both predicted using respective ANN models here. To reduce the number of design variables and facilitate fabrication, the elements are grouped into several material patches 36,37 . Substantial number of sample data can be generated offline, which enables the development of collaborative training and parallel computing in an efficient and effective way.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the number of design variables and facilitate fabrication, the elements are grouped into several material patches. 36,37 Substantial number of sample data can be generated offline, which enables the development of collaborative training and parallel computing in an efficient and effective way. To reduce the number of training samples and achieve sufficient regression precision, an active learning strategy is introduced here by providing active and sub-active samples in the next training round.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…The second requirement for the modelling design tool is that a specialized optimization algorithm, e.g., generic algorithm (GA), is needed to accelerate the search for an optimal morphology, similar to the optimization design of pristine FRCs [35][36][37]. That is because the exhaustive trials, even based on predictive modelling, are also unacceptable due to the complex and variable effects of CNT-network morphology on the damage mechanisms [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Strength and Toughness of Hierarchical Composites through Optimization of Position and Orientation of Nanotubes: A Computational Study

2020

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Hierarchical composites that combine microscopic fibers and carbon nanotubes (CNTs) offer opportunities to further improve mechanical properties. Motivated by the experimental evidence that the spatial distribution of CNTs has a significant effect on the strength and toughness of these composites, we developed a novel modelling tool to help us explore mechanisms of strengthening and toughening in an efficient way. The spatial position and orientation of CNTs are chosen as design variables and their optimization is performed on the example of a unidirectional fiber-reinforced composite (FRC) subjected to transverse tensile loading. The model relies on the use of genetic algorithm and finite element method. Our modelling results show that the CNT network with an optimized morphology suppresses stress concentrations in the matrix near the fibers. The optimized morphology is shown to activate a new strengthening and toughening mechanism—diffusion of damage at micro-scale. It allows substantial increase in the consumption of the strain energy by matrix cracking, delocalization of damage, and with it, improvement of the strength and toughness. When the network morphology of 1.0 wt% of CNTs is optimized, the strength and toughness are increased by 49% and 65%, respectively, compared to the pristine FRC. The same amount of homogenously distributed CNTs in the composite leads to only 2% of the strength increase accompanied by a 13% decrease in toughness. The work emphasizes the importance of optimizing spatial position and orientation of CNTs for the strength and toughness improvements of composites.

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“…Carbon fibers are uniformly distributed in the matrix and bear the most loads, whereas the base bonds the carbon fibers together. In practical engineering structures, CFRP is usually designed as a multi-perspective and multilayer material to make full use of the load bearing of carbon fibers in all directions [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

Research on Finite Element Model Modification of Carbon Fiber Reinforced Plastic (CFRP) Laminated Structures Based on Correlation Analysis and an Approximate Model

Zhang¹,

Yang²,

Du³

et al. 2019

Materials

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Carbon fiber reinforced plastic (CFRP) laminated structures have been widely used in modern engineering due to their excellent material properties, especially in the aerospace and shipping industries. This requires a high-accuracy finite element model of CFRP laminated structures. However, it is difficult to master the mechanical properties of CFRP structures comprehensively and accurately due to influences from multiple aspects, such as dispersion of material properties, uncertainty of manufacturing technologies, etc. Therefore, a finite element model modification method of CFRP laminated structures based on correlation analysis and an approximate model was proposed. Aiming at minimizing the difference between the analysis model and the measured inherent frequency, the proposed method improves the finite element modeling accuracy of CFRP laminated structures, by iterative optimization based on a global optimization algorithm. In order to solve the problem of high spatial dimension and slow searching in modification of CFRP laminated structure models, the Pearson correlation analysis method was used to screen the material parameters which exert significant impacts on frequency characteristics to reconstruct the searching space. Based on significance parameters, an approximate response model of the CFRP laminated structural system was established. Meanwhile, the modeling accuracy of different orders of response surface models (RSM) and a radial basis function (RBF) neural network model was analyzed, and the best approximate modeling scheme was obtained. The approximate model was updated based on the multi-island genetic algorithm (MIGA) to modify the finite element model of the CFRP laminated structure model. The maximum error and mean error of the updated model are 1.47% and 0.45%. It was proved that the material parameters modified by the proposed method are applicable to simulation analysis of the CFRP laminated structure.

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Discrete topology optimization of ply orientation for a carbon fiber reinforced plastic (CFRP) laminate vehicle door

Cited by 73 publications

References 28 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

Enhancing Strength and Toughness of Hierarchical Composites through Optimization of Position and Orientation of Nanotubes: A Computational Study

Research on Finite Element Model Modification of Carbon Fiber Reinforced Plastic (CFRP) Laminated Structures Based on Correlation Analysis and an Approximate Model

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