Transverse permeability determination and influence in resin flow through an orthotropic medium in the RTM process

Härter, Fabiane Valente; Souza, Jeferson Ávila; Isoldi, Liércio André; Santos, Elizaldo Domingues dos; Amico, Sandro Campos

doi:10.1590/s1517-707620170002.0167

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Aiming to provide a wide spectrum of data for the statistical models, data from a large number of published references on RTM and liquid resin infusion (LRI) [12,16,17,23,25,26,[34][35][36][37][38][39][40][41][42][43][44][45][46][47] are aggregated for the statistical analysis as documented in the supplementary material. The kinds of reinforcements investigated are plain weave (PW) 1 Â 1, a biaxial (0/90) noncrimp fabric (NCF), a unidirectional reinforcement (Uni), and a random fiber mat (Random).…”

Section: Materials and Processing Parametersmentioning

confidence: 99%

“…The reinforcement determines the resistance to the resin flow, which defines permeability [12][13][14] based on Darcy's law. [15][16][17] The flow behavior can be used to control the formation and location of defects. [18,19] According to Patel et al, [20] viscous drag forces among tows act at high-flow front velocity, impregnating the inter-tows regions firstly, and hence increasing the creation of microscopic porosity inside tows.…”

Section: Introductionmentioning

confidence: 99%

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The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis

et al. 2022

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This work proposes an approach combining artificial neural networks (ANN) with statistical models to predict injection processing conditions for four reinforcement architectures: plain weave, bidirectional noncrimp fabrics, unidirectional fabrics (Uni) and random fiber mats (Random). Key results allow evaluating the velocity of the flow front by combining processing parameters and creating a three‐dimensional response surface based on a properly trained ANN. This investigation is based on a large number of experimental results. The key role played by some physical parameters was associated with predicting the impregnation behavior (velocity of the flow front) during resin injection. The main outcome aims to provide a better control of void content in terms of size and position to the four fibrous reinforcements considered.

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Section: Materials and Processing Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis

et al. 2022

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“…In these processes, the final quality of the manufactured pieces, in terms of mechanical properties, is highly influenced by the correct specification and control of process parameters such as injection pressure and positioning of inlet/outlet vents. Moreover, control and modeling of the thermophysical properties of the resin and reinforcement medium is also important, thus many studies on resin viscosity [1][2][3] and reinforcement permeability [4][5][6][7][8] are also found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the resin transfer molding process including viscosity dependence with time and temperature

et al. 2021

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Flow behavior inside the mold cavity of liquid molding processes such as resin transfer molding (RTM) is important information that is necessary to determine filling time and void formation. Most of the studies found in the literature use isothermal models with Newtonian fluids and constant viscosities. However, for some specific applications, the mold filling time dependence on temperature and the viscosity dependence on time and temperature must be considered to precisely predict the flow advance inside the mold. In this study, a viscosity model, that accounts for temperature and time dependence is coupled with a standard computational fluid dynamics (CFD) model to simulate the resin advance inside an RTM mold cavity. The model is simpler than similar methods that describe viscosity as a function of temperature and resin conversion. Nevertheless, the results show that the proposed model is capable of calculating flow advance, air and resin temperatures, and viscosity changes with time and temperature as expected in actual RTM and correlated processing of thick parts or with low injection pressure or high fiber content.

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“…Through experimental verification, it was found that when the capillary number is between 1 × 10 −3 and 1 × 10 −6 , the capillary pressure plays a leading role, and the inner flow of fiber bundles is faster. Härter et al used PAM-RTM software to simulate the flow of resin in reinforcing medium and made a numerical study on the influence of transverse permeability of resin flow [ 39 ]. Maung et al studied the influence of fabric weaving mode on the duration of the vacuum impregnation process in the PAM-RTM program and simulated the porosity of different materials [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

A General and Efficient Approach for the Dual-Scale Infiltration Flow Balancing in In Situ Injection Molding of Continuous Fiber Reinforced Thermoplastic Composites

Liang

2021

Polymers

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In situ injection molding of continuous fiber reinforced thermoplastic composites is challenged by unbalanced dual-scale infiltration flow due to the pronounced capillary effect. In this paper, a general and efficient approach was proposed for dual-scale infiltration flow balancing based on numerical simulation. Specifically, Stokes and Brinkman equations were used to describe the infiltration flow in inter- and intra-fiber bundles. In particular, capillary pressure drop was integrated in the Brinkmann equation to consider the capillary effect. The infiltration flow front is tracked by the level set method. Numerical simulation and experimental results indicate that the numerical model can accurately demonstrate the unbalanced infiltration flow in inter- and intra-fiber bundles caused by the changes of the injection rate, the resin viscosity, the injection rate, the fiber volume fraction and the capillary number. In addition, the infiltration flow velocity in inter- and intra-fiber bundles can be efficiently tuned by the capillary number, which is mainly determined by the injection rate for a specified resin system. The optimal capillary numbers obtained by simulation and experiment are 0.022 and 0.026, which are very close to each other. Finally, one-dimensional in situ injection molding experiments with constant injection pressure were conducted to prepare fiber reinforced polymerized cyclic butylene terephthalate composite laminate with various flow rates along the infiltration direction. The experimental results confirmed that the lowest porosity and the highest interlaminar shear strength of the composite can only be obtained with the optimized capillary number, which is basically consistent with the simulation results.

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Transverse permeability determination and influence in resin flow through an orthotropic medium in the RTM process

Cited by 6 publications

References 22 publications

The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis

The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis

Modeling of the resin transfer molding process including viscosity dependence with time and temperature

A General and Efficient Approach for the Dual-Scale Infiltration Flow Balancing in In Situ Injection Molding of Continuous Fiber Reinforced Thermoplastic Composites

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