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Process simulation is frequently adopted to facilitate the optimization of the resin transfer molding process. However, it is computationally costly to simulate the multi-physical, multi-scale process, making it infeasible for applications involving huge datasets. In this study, the application of K-nearest neighbors and artificial neural network metamodels is proposed to build predictive surrogate models capable of relating the mold-filling process input-output correlations to assist mold designing. The input features considered are the resin injection location and resin viscosity. The corresponding output features investigated are the number of vents required and the resultant maximum injection pressure. Upon training, both investigated metamodels demonstrated desirable prediction accuracies, with a low prediction error range of 5.0% to 15.7% for KNN metamodels and 6.7% to 17.5% for ANN metamodels. The good prediction results convincingly indicate that metamodeling is a promising option for composite molding applications, with encouraging prospects for data-intensive applications such as process digital twinning. [ABSTRACT FROM AUTHOR]

This paper addresses the development of a digital twin, based on an inversion procedure, integrating process monitoring with simulation of composites manufacturing to provide a real time probabilistic estimation of process outcomes. A computationally efficient surrogate model was developed based on Kriging. The surrogate model reduces the computational time allowing inversion in real time. The tool was implemented in the filling stage of an resin transfer molding processing of a carbon fiber reinforced part resulting in the probabilistic prediction of unknown parameters. Flow monitoring data were acquired using dielectric sensors. The inverse scheme based on Markov Chain Monte Carlo uses input parameters, such as permeability and viscosity, as unknown stochastic variables. The scheme enhances the model by reducing model parameter uncertainty yielding an accurate on line estimation of process outcomes and critical events such as racetracking. The inverse scheme provides a prediction of filling duration with an error of about 5% using information obtained within the first 30% of the process. [ABSTRACT FROM AUTHOR]

Resin transfer molding and vacuum-assisted resin transfer molding are two of the most commonly used liquid composite molding processes. For resin transfer molding, mold filling simulations can predict the resin flow patterns and location of voids and dry spots which has proven useful in designing the mold and injection locations for composite parts. To simulate vacuum-assisted resin transfer molding, even though coupled models are successful in predicting flow patterns and thickness distribution, the input requires fabric compaction characterization in addition to permeability characterization. Moreover, due to the coupled nature of flow and fabric compaction, the simulation is computationally expensive precluding the possibility to optimize the flow design for reliable production. In this work, we present an alternative approach to characterize and use an "effective" permeability in the resin transfer molding solver to simulate resin flow in vacuum-assisted resin transfer molding. This decoupled method is very efficient and provides reasonable results. The deviations in mold filling times between experiments and simulations for the resin transfer molding process with E-glass CSM and carbon 5HS were 4.7% and 1.0%, respectively, while for the vacuum-assisted resin transfer molding case using "effective permeability value" with E-glass CSM and carbon 5HS fabrics were 11.1% and 12.3%, respectively, which validates the approach presented. [ABSTRACT FROM AUTHOR]

Resin flow during Vacuum Assisted Resin Transfer Molding (VARTM) process, when distribution media (DM) is placed on top of the reinforcement, is largely affected by variation in through the thickness permeability of a woven fabric. The variation in permeability is due to the random pinhole regions around the junctions of fiber tows as they are woven together to form a fabric preform. We characterize and model this pinhole effect on resin flow with the aim of exploring the role of DM permeability ( K DM ) on void formation. It was found that percentage of voids increases with higher K DM . Five hundred simulations were executed for low, medium and high K DM values and their effect on resin flow and void formation was investigated. Twenty experiments for each DM case were conducted. Flow along the bottom surface was recorded with time. It was observed that the flow front along the bottom became more uneven and irregular with higher K DM , which resulted in higher percentage of voids formed during the process. The numerical simulations qualitatively and quantitatively agreed with the experimentally measured behavior exhibiting higher percentage of unfilled region with increasing DM permeability. [ABSTRACT FROM AUTHOR]

This study set out to investigate the applicability of data assimilation to the monitoring of the flow of non-planar 3D shapes through the integration of visual observations with a stochastic numerical simulation of the resin flow in a vacuum-assisted resin-transfer molding process. First, we investigated the effect of the image resolution on the flow-monitoring estimation performance. By means of numerical experiments using images of various resolutions, we verified the applicable range of the image resolution in the proposed technique. Furthermore, the proposed technique was applied to 3D structures such as curved surfaces or ribbed structures. Using the visual observation values, the impregnation distances obtained from the image data were calculated in combination with the shape of the molding. The results confirmed that our proposed method is capable of successfully estimating the 3D impregnation behavior and permeability fields. [ABSTRACT FROM AUTHOR]

The present study investigated a numerical simulation of molding-defect formation during resin transfer molding using boundary element method and line dynamics. The proposed method enables to simulate small molding defects by increasing the node for required position during time evolution; thereby, the method computes high-resolution flow front without being affected by the initial mesh geometry. The method was applied to the radial injection RTM with single inlet, and it was confirmed by comparison with theoretical value based on Darcy’s law that the flow advancement was computed with high accuracy. In addition, the method was also applied to the flow advancement for inclusion problem with cylinder, and four-point injection problem. The simulated flow behavior, void formation, and shrinkage agreed with the results in references. Finally, the method was compared with experiments using two-point injection problem. The computed configuration of the flow front and weld line agreed well with the experimental results. [ABSTRACT FROM AUTHOR]

The quality of composite parts manufactured by resin transfer molding is sensitive to material and process variations during the preform impregnation. In order to improve the process robustness in two-dimensional injection cases, this work proposes a fast method for tracking and controlling the resin flow through the preform, using only a small number of pressure sensors embedded in the mold. The approach combines pressure signals and flow modeling in a quick algorithm that returns on-line estimations of the flow front profiles. Virtual and real injection tests demonstrated the accuracy of the methodology and provided information for designing the sensing system. Moreover, the investigation proved the feasibility of a simple and effective procedure of flow rate control. Based on a computer-controlled pressure regulator acting on the inlet gate, a feedback mechanism was implemented in the system and allowed keeping the flow front velocity within a target range of values. Such a control procedure may be used to limit the formation of voids and enhance the final part quality. [ABSTRACT FROM AUTHOR]

Two-dimensional finite-element method solution of the post-filling stage of vacuum infusion was studied based on mass conservation in an infinitesimal control volume. First, resin pressure distribution at the instant of mold filling was calculated and then used as the initial condition for the transient post-filling stage. Explicit time-marching algorithm was used for the evolution of resin pressure and part thickness, and its stability was ensured by selecting the time step adaptively. Finite-element method solution was verified analytically for one-dimensional case and numerically for two-dimensional cases using global mass conservation. The time that it took for the settlement of pressure and thickness was investigated to compare the effectiveness of different resin-bleeding scenarios where different number and locations of gates were used. It was shown that the settlement time increased exponentially as the dimensions of the mold increased, which proved that process simulation fed with correctly designed material characterization can replace tedious trial-and-error search of control actions to reduce the settlement time and variation in part thickness. [ABSTRACT FROM AUTHOR]

A new finite element formulation was developed to simulate the prepreg resin impregnation effect in vacuum-assisted resin infusion/prepreg co-curing process. The numerical model combined the fiber compaction and resin flow model in prepreg part with resin impregnation front tracking model in dry fiber fabric. The influences of processing parameters, including various temperature and time, on the impregnation height in dry fiber fabric and change of fiber volume fraction in prepreg stack were analyzed by numerical model and micrographs in experiment. The accuracy of numerical model was validated by the good agreement between simulation and experimental results. These results are greatly helpful to optimize processing parameters and improve the manufacturing efficiency in co-cured vacuum-assisted resin infusion process. [ABSTRACT FROM AUTHOR]

Compression resin transfer molding (CRTM) is an effective process for the manufacturing of composite parts with large size and high fiber content. The analysis of the resin flow and stress distributions can only be performed by directly solving the coupled flow/deformation equations, but it is difficult to handle the complicated preform deformation models and geometry models; therefore, the simulation precision and application range are extremely limited. In this paper, an alternative approach is introduced to overcome the above problems, in which the preform deformation and the accompanying resin release during the secondary compaction phase are calculated in an additional element associated with each unit of the discretized model geometry instead of solving the coupled governing equations directly, so the complex compaction models can be adopted. Three simulation examples are presented to demonstrate the accuracy and capability of the above numerical approach on velocity-controlled, force-controlled 3D CRTM processes. [ABSTRACT FROM PUBLISHER]

Abstract: Cost effective manufacturing of high performance polymer matrix composite structures is an important consideration for the growth of its use. Resin transfer moulding (RTM) and vacuum assisted resin transfer moulding (VARTM) are the efficient processes for the cost effective manufacturing. These processes involve transfer of resin from the tank into the reinforcing preform loaded into a closed mould. Resin flow within the preform and reinforcement wetting can be characterized using the permeability properties. Different reinforcement and resin properties and process parameters affecting the permeability are discussed based on state of art literature review covering experimental studies. General theory for the determination of permeability is presented. Based on the literature review, permeability values for different reinforcement architecture, resin and processing conditions are presented. Further, possible sources of error during experimental determination of permeability and issues involved with reproducibility are discussed. [Copyright &y& Elsevier]

In this paper, recent shell model is advanced towards the calibration and validation of the Vacuum-assisted Resin Transfer Molding (VARTM) process in a novel way. The model solves the nonlinear and strongly coupled resin flow and preform deformation when the 3-D flow and stress problem is simplified to a corresponding 2-D problem. In this way, the computational efficiency is enhanced dramatically, which allows for simulations of the VARTM process of large scale thin-walled structures. The main novelty is that the assumptions of the neglected through-thickness flow and the restricted preform deformation along the normal of preform surface suffice well for the thin-walled VARTM process. The model shows excellent agreement with the VARTM process experiment. With good accuracy and high computational efficiency, the shell model provides an insight into the simulation-based optimization of the VARTM process. It can be applied to either determine locations of the gate and vents or optimize process parameters to reduce the deformation. [ABSTRACT FROM AUTHOR]