Numerical Simulation and Multi-objective Optimization for Curing Process of Thermosetting Prepreg

Hou, Jiatong; You, Bo; Xu, Jiazhong; Fu, Tianyu; Wang, Tao

doi:10.1007/s10443-022-10022-7

Cited by 6 publications

(4 citation statements)

References 50 publications

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“…The internal heat source is the cross‐linking reaction of the resin component in the composite material, while the external one is the curing temperature in the autoclave. According to the 3D transient heat conduction equation, the distributions of temperature and degree of curing (DoC) in composites at each curing time can be calculated as follows 15 :

λ_{x} \frac{\partial^{2} T}{\partial x^{2}} goodbreak+ λ_{y} \frac{\partial^{2} T}{\partial y^{2}} goodbreak+ λ_{z} \frac{\partial^{2} T}{\partial z^{2}} goodbreak+ \overset{Q}{true} goodbreak= ρ_{c} C_{c} \frac{\partial T}{\partial t}

\begin{matrix} true \\ \dot{Q} = ρ_{normalr} V_{normalr} H_{normalr} \frac{italicdα}{italicdt} \end{matrix}

Where,

λ_{x}, λ_{y}, λ_{z}

are the thermal conductivity of the composite material in the three directions of X , Y and Z , respectively. T is the temperature in the autoclave, and

\overset{Q}{true}

is the instantaneous heat generated by the internal heat source.…”

Section: Theoretical Modelmentioning

confidence: 99%

“…The internal heat source is the cross-linking reaction of the resin component in the composite material, while the external one is the curing temperature in the autoclave. According to the 3D transient heat conduction equation, the distributions of temperature and degree of curing (DoC) in composites at each curing time can be calculated as follows 15 :…”

Section: Curing Kinetic Modelmentioning

confidence: 99%

“…Where, F is the functional in Formula (15). In this way, the geodesic equation between different points on the corresponding surface can be obtained, then the entire surface can be covered by projection, translation in the specified direction, and reprojection.…”

Section: Design Of Vs Ply Based On Geodesic Curvesmentioning

confidence: 99%

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Influence of variable‐stiffness layup configuration on curing deformation of curved‐surface structures

Niu

Yao

2022

J of Applied Polymer Sci

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Accurate prediction the curing‐induced deformation is the key to the fabrication of high‐quality composite structures. Aiming at controlling shape distortion of curved‐surface parts during curing process, a layup optimization strategy is proposed based on a variable stiffness (VS) placement method, which uses geodesic curves as fiber‐tow paths. Taking an offset parabolic antenna reflector (PAR) for example, chemical shrinkage and thermal deformation of curved‐surface composites during the curing process and after demolding are modeled. By combining Fourier heat conduction theory, curing kinetics, and generalized Maxwell viscoelastic model, the interactions between chemical cross‐link reaction, heat transfer, and mechanical deformation are taken into account. By comparing the deformation characteristics of VS and traditional constant stiffness structures, the proposed simulation strategy of curing process for composites can predict the factors that influence the curing deformation of VS structures are analyzed. The results show that the maximum of VS PAR decreases by 11.02%, and the average magnitude decreases by 32.35%.

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λ_{x} \frac{\partial^{2} T}{\partial x^{2}} goodbreak+ λ_{y} \frac{\partial^{2} T}{\partial y^{2}} goodbreak+ λ_{z} \frac{\partial^{2} T}{\partial z^{2}} goodbreak+ \overset{Q}{true} goodbreak= ρ_{c} C_{c} \frac{\partial T}{\partial t}

\begin{matrix} true \\ \dot{Q} = ρ_{normalr} V_{normalr} H_{normalr} \frac{italicdα}{italicdt} \end{matrix}

Where,

λ_{x}, λ_{y}, λ_{z}

are the thermal conductivity of the composite material in the three directions of X , Y and Z , respectively. T is the temperature in the autoclave, and

\overset{Q}{true}

is the instantaneous heat generated by the internal heat source.…”

Section: Theoretical Modelmentioning

confidence: 99%

Section: Curing Kinetic Modelmentioning

confidence: 99%

See 1 more Smart Citation

Influence of variable‐stiffness layup configuration on curing deformation of curved‐surface structures

Niu

Yao

2022

J of Applied Polymer Sci

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show abstract

“…Hou et al utilized machine learning techniques to predict the temperature difference and degree of cure difference during the curing process of composites. [ 21 ] Additionally, Özkan et al used neural networks to accurately predict the mechanical properties of nanocomposite films. [ 22 ] The authors' previous work discusses the application of binary search (BS), Linear Regression, and Neural Networks to improve the computational efficiency of predicting damage initiation.…”

Section: Introductionmentioning

confidence: 99%

Determining damage initiation of carbon fiber reinforced polymer composites using machine learning

et al. 2022

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Computational progressive failure analysis (PFA) is vital for the analysis of carbon fiber reinforced polymer (CFRP) composites. The damage initiation criterion is one of the essential components of a PFA code to determine the transition of a material's state from pristine or microscopically damaged to macroscopically damaged. In this paper, data-driven models are developed to determine the matrix damage initiation with the objective to save computation time. For 2D plane stress states, the computational cost for determining damage initiation can be dramatically reduced by implementing a binary search (BS) algorithm and predictive machine learning models. Machine learning models are evaluated against a regression problem of predicting damage crack angle as well as against a classification problem for predicting damage initiation outright. It is found that regression models perform much better for plane stress and 3D stress states when generating failure envelopes. In both cases, a neural network is able to produce a failure envelope that is over 99% accurate while reducing computational cost by over 90%.

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Multiobjective optimization of resin transfer molding curing process for silicon‐containing arylacetylene resin‐matrix composites

Jin,

Wang,

Zhu

et al. 2024

Polymers for Advanced Techs

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Silicon‐containing arylacetylene resin (PSA)‐matrix composites hold great potential for aerospace applications due to their excellent heat resistance. In recent years, many PSAs with specific functions have been designed via materials genome approach (MGA), and appropriate resin transfer molding (RTM) curing processes need to be screened to strike a balance between low cost and high quality. In this study, a novel tool based on finite element curing simulation and multiobjective genetic algorithm was developed to optimize the RTM curing process for novel PSA‐matrix composites. The silicon‐containing fluorenylacetylene resin (PSA‐VBF) was selected as the object to systematically characterize its apparent curing kinetics. To address the problem of explosive polymerization of the resin at the injection port during the RTM process, a multiobjective optimization of the curing process using a genetic algorithm was performed to obtain the Pareto front with the maximum temperature gradient at the injection port of the resin, the maximum degree of cure gradient of the composites, and the process time as the objectives. A global sensitivity analysis was also conducted to identify the key parameters. The results demonstrate that the optimized curing process can significantly reduce the temperature gradient and the curing degree gradient with improved curing efficiency.

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Numerical Simulation and Multi-objective Optimization for Curing Process of Thermosetting Prepreg

Cited by 6 publications

References 50 publications

Influence of variable‐stiffness layup configuration on curing deformation of curved‐surface structures

Influence of variable‐stiffness layup configuration on curing deformation of curved‐surface structures

Determining damage initiation of carbon fiber reinforced polymer composites using machine learning

Multiobjective optimization of resin transfer molding curing process for silicon‐containing arylacetylene resin‐matrix composites

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