Experimental validation of post-filling flow in vacuum assisted resin transfer molding processes

In Liquid Composite Molding (LCM) processes with compliant tool, such as Vacuum Assisted Resin Transfer Molding Process (VARTM), resin flow continues even after the inlet is closed due to the preform deformation and pressure gradient developed during infusion. The resin flow and thickness changes continue until the resin pressure becomes uniform or the resin gels. This post-filling behavior is important as it will determine the final thickness and fiber volume fraction distribution in the cured composite. In this paper, a previously proposed one dimensional coupled flow and deformation process model has been compared with the experimental data in which the resin pressure and part thickness at various locations during the post-filling stage is recorded. Two different post-infusion scenarios are examined in order to determine their impact on the final part fiber volume fraction and thickness. The effects of different venting arrangements are demonstrated. The model predictions compare favorably with the experimental data, with the minor discrepancies arising due to the variability of material properties.     

On-line strategic control of liquid composite mould filling process

Liquid composite moulding (LCM) processes are used to manufacture high quality and complex-shaped fibre reinforced polymeric composite parts in the aerospace, automotive, marine and civil industries. A thermoset resin is injected into a mould cavity filled with a reinforcing fibrous preform. The composite part is demoulded after the filling is completed and resin has cured. During prototype development, the design engineers may combine their manufacturing experience with simulations to decide which LCM process must be used for the selected part. For complicated mould shapes, the manufacturing engineer has to make decisions about injection pressure, flow rate, location of gates and vents, etc. to achieve a high-quality composite part which is free of dry spots. Inherent variability in the process and the possible errors in characterization of material properties, such as fibre volume fraction and permeability, challenge the manufacturing engineer to reduce the number of unacceptable parts. An on-line strategic controller with in situ sensor data can influence the flow front pattern during mould filling and drive the process towards successful completion. Some of these variabilities are considered in off-line mould filling simulations. By analysing the simulation results, the sensors are placed inside the mould to identify the variabilities and take corrective action(s) to eliminate voids. Sensor data and the control actions are cast in the form of a decision tree. Data acquisition software collects the in situ sensor data and implements the control actions from this decision tree. A case study was included in which various race-tracking and bulk permeability variations can be expected during manufacturing. The proposed controller is described in detail for this selected case study and its usefulness is verified with experiments.     

Characterization of preform permeability in the presence of race tracking

For realistic simulation of resin flow in a stationary fibrous porous preform during Liquid Composite Molding (LCM) processes, it is necessary to input accurate material data. Of great importance in simulating the filling stage of the LCM process is the preform permeability; a measure of the resistance the preform poses to the flowing fluid. One method to measure permeability values is by conducting one-dimensional flow experiments, and matching the flow behavior to known analytical models. The difficulty is the edge effects such as race tracking disrupt the flow and violate the one-dimensional flow assumption. The new approach outlined in this paper offers a methodology to obtain accurate bulk permeability values despite any race tracking that may be present along the edges of the mold containing isotropic fabrics. Further, a method of approximate equivalent isotropic scaling is explained to extend the use of this method to determine permeability of anisotropic materials with race tracking present. Both approaches are validated with computer simulations, and then utilized in laboratory experimentation. The values calculated from this approach compare well with permeability values obtained from one-dimensional permeability experiments without the presence of race tracking.     

Numerical prediction of saturation in dual scale fibrous reinforcements during Liquid Composite Molding

This paper presents a fractional flow model based on two-phase flow, resin and air, through a porous medium to simulate numerically Liquid Composites Molding (LCM) processes. It allows predicting the formation, transport and compression of voids in the modeling of LCM. The equations are derived by combining Darcy’s law and mass conservation for each phase (resin/air). In the model, the relative permeability and capillary pressure depend on saturation. The resin is incompressible and the air slightly compressible. Introducing some simplifications, the fractional flow model consists of a saturation equation coupled with a pressure/velocity equation including the effects of air solubility and compressibility. The introduction of air compressibility in the pressure equation allows for the numerical prediction of the experimental behavior at low constant resin injection flow rate. A good agreement was obtained between the numerical prediction of saturation in a glass fiber reinforcement and the experimental observations during the filling of a test mold by Resin Transfer Molding (RTM).     

Dry fiber automated placement of carbon fibrous preforms

The superior material properties of carbon fiber-reinforced composites make them especially attractive for applications in aeronautics and aerospace industries. Cost reduction and time saving are continuously driving industry, leading to new industrial challenges which include manufacturing composite structures with optimal mechanical performances using the potential of advanced processes using robotics.To produce complex part shapes, technologies implying fabric draping in a mold imply large waste amount, fabric structure variability and uncertainties concerning local fiber volume fraction amount and thus final mechanical properties. To overcome such issues and comply with cost and time efficiency, automated dry fiber placement for preform manufacturing is proposed. This approach allows to integrate many functions in a complex part thank to the ability of the robot to steer fiber tows at specific locations. The final composite part is obtained by injecting the produced preform with resin using RTM (Resin Transfer Molding) or infusion process.The presented project aims to define the influence of the process driving parameters during fiber placement on the final preform properties range. Preforms were produced using a lab-scale automated placement demonstrator. Three preforms configurations were tested to highlight the influence of the preform structure on permeability and mechanical parameters through characterization of the compression behavior and permeability of the produced preforms. Choice of configuration will affect mechanical properties on the manufactured preforms, whereas creation of open channels to enhance the flow propagation during manufacturing does not necessarily increase the preform permeability.     

Use of Centroidal Voronoi Diagram to find optimal gate locations to minimize mold filling time in resin transfer molding

In Resin Transfer Molding (RTM) processes, liquid resin is injected into a dry reinforcement structure to create a composite part within given time limits. To reduce the fill time, resin may be injected into the mold through multiple gates. The minimum number of gates and their locations needs to be determined. To reduce the number of scenarios to be simulated, an iterative method is implemented for multiple-gate injection optimization. The inlet nodes on the mesh surface are used to generate a Voronoi Diagram of the mold geometry. Then the optimal Centroidal Voronoi Diagram (CVD) of the mold surface is searched iteratively. It is shown that the generation points associated with the optimal CVD correspond with the gate locations that yield the shortest fill time. The results are compared with exhaustive search and genetic algorithms results to illustrate the efficiency and accuracy of CVD method.     

Experimental investigation and flow visualisation of the resin transfer mould filling process for non-woven hemp reinforced phenolic composites

Resin transfer moulding (RTM) of glass fibre reinforced polymeric composites offers the advantages of automation, low cost and versatile design of fibre reinforcement. A replacement of glass fibres with natural plant fibres as reinforcement in polymeric composites provides additional technological, economical, ecological and environmental benefits. The resin transfer mould filling process has significant effects on different aspects, such as fibre wetting out and impregnation, injection gate design, “dry patch” and void formation. Flow visualisation experiments were carried out using a transparent RTM mould to develop a better understanding of the mould filling process for hemp mat reinforced phenolic composites. The mould filling of unreinforced phenolics was characterised by a “quasi-one-dimensional steady state” flow. In the case of hemp non-woven reinforced system, the mould filling process can be considered as the flow of fluids through porous media. “Fibre washing” was a typical problem encountered during the injection process, leading to poor property uniformity. In addition, a preferential flow path was usually created near the edges and corners of the mould. The path exhibited low flow resistance and caused the resin flow front to advance much faster in these regions. The edge flow disturbed the steady flow, leading to difficulties in venting arrangement and “dry patch” formation. The edge flow and fibre washing were alleviated by reinforcement manipulation so steady state flow could be achieved. The relationships between the filling time and injection pressure and between filling time and different fibre weight fractions have been established for certain specific injection strategies.     

Resin flow analysis with fiber preform deformation in through thickness direction during Compression Resin Transfer Molding

Resin flow during Compression Resin Transfer Molding (CRTM) can be best described and analyzed in three phases. In the first phase, a gap is created by holding the upper mold platen parallel to the preform surface at a fixed distance from it. The desired amount of resin injected into the gap quickly flows primarily over the preform. The second phase initiates when the injection is discontinued and the upper mold platen moves down squeezing the resin into the deforming preform until the mold surface comes in contact with the preform. Further mold closure during the final phase will compact the preform to the desired thickness and redistribute the resin to fill all empty spaces. This paper describes the second phase of the infusion. We assume that at the end of phase one; there is a uniform resin layer that covers the entire preform surface. This constrains the resin to flow in through the thickness direction during the second phase. We model this through the thickness flow as the load on the upper mold forces the resin into the preform, simultaneously compacting the preform. The constitutive equations describing the compaction of the fabric as well as its permeability are included in the analysis. A numerical solution predicting the flow front progression and the deformation is developed and experimentally verified. Non-dimensional analysis is carried out and the role of important non-dimensional parameters is investigated to identify their correlations for process optimization.     

Three-dimensional reconstruction of resin flow using capacitance sensor data assimilation during a liquid composite molding process: A numerical study

Liquid composite molding (LCM) is a method to manufacture fiber-reinforced composites, where dry fabric reinforcement is impregnated with a resin in a molding apparatus. However, the inherent process variability changes resin flow patterns during mold filling, which in turn may cause void formation. We propose a method to reconstruct three-dimensional resin flow in LCM, without embedding sensors into the composite structure. Capacitance measured from pairs of electrodes on molding tools and the stochastic simulation of resin flow during an LCM process are integrated by a sequential data assimilation method based on the ensemble Kalman filter; then, three-dimensional resin flow and permeability distribution are estimated simultaneously. The applicability of this method is investigated by numerical experiments, characterized by different spatial distributions of permeability. We confirmed that changes in resin flow caused by spatial permeability variations could be captured and the spatial distribution of permeability could be estimated by the proposed method.     

Simulation and experimental validation of flow flooding chamber method of resin delivery in liquid composite molding

A new method of resin delivery, which we refer to as the flow flooding chamber (FFC), is investigated to improve infusion time and reduce material waste associated with the Vacuum Assisted Resin Transfer Molding (VARTM) process. The FFC method uses a rigid chamber that rests on top of the bagging material and a vacuum drawn inside the chamber stretches the bag to take the shape of the chamber above the fiber preform. Resin is then drawn into this chamber unimpeded, and once the chamber is full of resin, the release of the vacuum results in application of atmospheric pressure on top of the bag that drives the resin into the fiber preform. The distribution media and other subsequent materials for its removal are not needed in this modified VARTM process. This process is mathematically modeled using a two event model that couples them by using the output conditions from the first event to the input conditions of the second event. The model is implemented in a numerical simulation so one can track the movement of the resin into the chamber and the preform. Experiments using the FFC process are conducted in complex geometries containing inserts and the flow fronts and fill times are recorded. The results compare very well with the predictions validating the assumptions made in the model to describe the flow.     

“Equivalent” permeability and flow in compliant porous media

Resin flow modeling for liquid composite molding processes is generally based on assumptions of rigid porous media. This is invalid for process variations utilizing compliant mold. Yet the models built on rigid porous media assumption are used with some success in analyzing such infusions.Previous work showed that for certain porous media the one dimensional flow patterns are similar to those in rigid porous media and the deformation effects can be included in a scaling factor for permeability.This note analyzes the one-dimensional linear and radial flows in porous media with generic constitutive relations between resin pressure, thickness and permeability. It shows that as long as the deformation remains moderate, the effect of deforming porous medium may be incorporated in a single scaling factor for material permeability. This scaling factor depends on material and applied injection pressure, but does not change with time, flow-front position or type of infusion (linear or radial).     

Simulation of void formation in liquid composite molding processes

Air entrapment within and between fiber tows during preform permeation in liquid composite molding (LCM) processes leads to undesirable quality in the resulting composite material with defects such as discontinuous material properties, failure zones, and visual flaws. Essential to designing processing conditions for void-free filling is the development of an accurate prediction of local air entrapment locations as the resin permeates the preform. To this end, the study presents a numerical simulation of the infiltrating dual-scale resin flow through the actual architecture of plain weave fibrous preforms accounting for the capillary effects within the fiber bundles. The numerical simulations consider two-dimensional cross sections and full three-dimensional representations of the preform to investigate the relative size and location of entrapped voids for a wide range of flow, preform geometry, and resin material properties. Based on the studies, a generalized paradigm is presented for predicting the void content as a function of the Capillary and Reynolds numbers governing the materials and processing. Optimum conditions for minimizing air entrapment during processing are also presented and discussed.     

Vent Location Optimization Using Map-Based Exhaustive Search in Liquid Composite Molding Processes

In liquid composite molding (LCM) processes, the resin is injected into the mold cavity, which contains preplaced reinforcement fabrics, through openings known as gates, while the displaced air leaves the mold through openings called vents. Under nominal conditions, the last points to fill are chosen as vent locations. However, due to imperfect preform cutting and placement, gaps and channels may form along the edges and curvatures in a mold, offering a path with less resistance for resin flow. The faster advance of resin through these gaps and channels, a common disturbance known as racetracking, will cause the last filled regions to vary, which complicates the vent selection process. In this study, probabilistic racetracking modeling is used to capture last-filled region distribution over the mold geometry. Success criteria for mold filling are defined in terms of dry spot tolerances, and vent fitness maps, which display potential vent locations, are created. Next, exhaustive search algorithm is coupled with vent fitness maps to determine optimal vent configurations. The map-based exhaustive search is demonstrated on three geometries and results are compared with existing combinatorial search results. The performance of the optimal vent configurations is evaluated in a virtual manufacturing environment. Sensitivity analyses are conducted to determine the influence of optimization parameters on the results.     

An optically-based inverse method to measure in-plane permeability fields of fibrous reinforcements

Structural composite manufacturing relying on Liquid Composite Molding technologies is strongly affected by local variability of the fibrous reinforcement. Optical techniques using light transmission are used and allow field measurements of areal weight (and fibre volume fraction) of glass fibre reinforcement. The coupling of obtained areal weight mappings along with injection flow fronts is used to extract in-plane permeability fields. The current work presents results with a focus on glass random mats, but the method can be adapted to any glass fibrous medium. A study of convergence and error due to discretization is performed. Also the influence of the stacking of fibrous layers on the preform variability is analyzed. The major advantage of the proposed technique is a relatively fast acquisition of statistical data on reinforcement variability, which can be later utilized in stochastic based process simulations.     

Combinatorial Search to Optimize Vent Locations in the Presence of Disturbances in Liquid Composite Molding Processes

In liquid composite molding processes, the resin is injected into a mold cavity containing preplaced reinforcement fabrics through openings known as gates, and the air leaves the mold through openings called as vents. Under nominal conditions, the last points to fill are chosen as vent locations. However, due to imperfect preform placement, gaps and channels may form along the edges and at curvatures in a mold, offering a path with less resistance for resin flow. The faster advance of resin through these gaps and channels, a common disturbance known as racetracking, will cause the last filled regions to vary, which complicates the vent selection process. We introduce a methodology that uses set and probability theories to forecast racetracking conditions in a mold and have developed a combinatorial search algorithm to locate corresponding optimal vent locations. The accuracy, efficiency and usefulness of the approach is illustrated with three case studies.     

Flow analysis during compression of partially impregnated fiber preform under controlled force

Compression resin transfer molding (CRTM) is an alternative solution to conventional resin transfer molding processes. It offers the capability to produce net shape composites with fast cycle times making it conducive for high volume production. The resin flow during this process can be separated into three phases: (i) metered amount of resin injection into a partially closed mold containing dry fiber preform, (ii) closure of the mold until it is in contact with the fiber preform displacing all the resin into the preform and (iii) further mold closure to the desired thickness of the part compacting the preform and redistributing the resin. Understanding the flow behavior in every phase is imperative for predictive process modeling that guarantees full preform saturation within a given time and under specified force constraints.     

Simulation of coupling filtration and flow in a dual scale fibrous media

In this article, numerical simulation of suspension (particles filled-resin) flow through a fibrous media taking into account dual scale porosity in LCM (Liquid Composite Molding) processes is presented. During the flow, a strong interaction between the particle motion and the fluid flow takes place at the porous media wall (the fiber bundle surface). In this study, the Stokes–Darcy coupling is used to describe the resin flow at mesoscopic scale to treat the particles in suspension. A “fluid” model to describe the suspension flow, a “filtration” model to describe the particle capture and a “solid” model dedicated to the modeling of mass particles dynamics was used. The “solid” model is also operated to identify the particles retention.For validation, the numerical results of proposed model were compared with the experimental results from the literature and found in good agreement. Then, other numerical results studying the suspension’s rheological behavior are presented.     

Resin flow modeling in compliant porous media: an efficient approach for liquid composite molding

In Liquid Composite Molding (LCM) processes such as Resin Transfer Molding (RTM) and Vacuum Assisted Resin Transfer Molding (VARTM), complete saturation of reinforcement with resin during the injection step is necessary. In RTM, reinforcement experiences no deformation during infusion but for other methods reinforcement thickness changes during the injection. To model resin flow in compliant media, RTM flow simulation software is routinely used. It has been successful in predicting flow patterns if appropriate “effective” permeability is used. The proper approach requires new implementation that couples the deformation and pressure field which is computationally more demanding. Our work describes a computationally efficient methodology to add corrections into RTM simulation environment to account for deformation. This approach is verified with known solutions and experimental validation. The simulation is applied to a complex geometry which demonstrates better computational performance and confirms that the “effective” permeability may be used to model flow in complex geometries.     

Development of adaptive injection flow rate and pressure control algorithms for resin transfer molding

To prevent dry spot formation during fabrication of composite parts by Resin Transfer Molding (RTM), a control interface and four different adaptive control algorithms have been developed and tested with numerical simulations. The interface is capable of controlling the flow pattern of resin as it fills a mold containing a preform of fiber reinforcement, provided that the mold is equipped with multiple inlet gates, a single vent and a spinal sensor system that continuously feeds the interface with the resin flow front locations along the spine lines connecting the inlet gates to the vent. Four different adaptive control algorithms targeting on injection flow rate control, injection pressure control, linearly-corrected pressure control, and the combined flow rate and linearly-corrected pressure control have been proposed and incorporated with the control interface. To provide desirable controllability of the filling process and effective utilization of the resin dispensing equipment, the final formulations were optimized by means of numerical simulations of a rectangular RTM part containing different permeability distributions. The results were compared to investigate the strengths and weaknesses of the spinal adaptive control algorithms in terms of dry spot size, filling speed, and the minimum responding speed of injection pump. Finally, a complex geometry case study was conducted to validate and highlight the spinal adaptive control algorithms’ capability in handling flow disturbance for a complex RTM mold filling process which involves irregular mold geometry, multiple inserts, significant permeability and racetracking variations, and non-straight spinal sensors.     

An approach to couple mold design and on-line control to manufacture complex composite parts by resin transfer molding

During impregnation of the resin into a closed mold containing the preform in the resin transfer molding (RTM) process, increased yield of successful parts can be achieved if one could account for the inherent disturbances such as race-tracking and preform variability. One way to address this is to use sensors and actuators to control the resin flow dynamics during the impregnation process to counteract the disturbances. In this paper, we use a mold filling simulation tool to develop a design and control methodology that, with the help of sensors and actuators, identifies the flow disturbance and redirects the resin flow to successfully complete the mold filling process without any voids. The methodology is implemented and experimentally validated for a mold geometry that contains complex features such as tapered regions, rib structures, and thick regions. The flow modeling for features such as ribs and tapered sections are validated independently before integrating them into the mold geometry. The approach encompasses creation of software tools that find the position of the sensors in the mold to identify anticipated disturbances and suggest flow control actions for additional actuators at auxiliary locations to redirect the flow. Laboratory hardware is selected and integrated to automate the filling process. The effectiveness of the methodology is demonstrated by conducting experiments that, with feedback from the sensors, can automate and actively control the flow of the resin to consistently impregnate all the fibers completely despite disturbances in the process.