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Compression resin transfer moulding (CRTM) has been widely used to manufacture automotive parts with reduced production cycle times. With the development of fast curing thermosetting resins, the CRTM process is a viable option for the high production rates in the transportation industry. However, the dynamic resin curing behaviour poses a potential risk of manufacturing defects in the part. In order to reduce the risk during the development of the tool and the process parameters, this paper proposes a modelling framework for the CRTM process when using fast curing resin systems. The work specifically focused on the coupling between heat transfer, resin cure, resin flow and preform compaction using a commercial code, PAM-RTM. The tool captures accurately the preform filling, temperature and resin pressure evolution during the injection and compression phase. The application of the framework was demonstrated for a complex 3D demonstrator. The predicted preform filling had an accuracy of 73% for the flow front evolution compared to the experimental results. This work demonstrates the validity of the framework proposed when dealing with resin systems that are challenging to process.

Resin and fiber flow around the injection nozzle of a flat mold in injection molding was analyzed using the particle method that can easily track resin and fiber flow, and the effects of injection position of resin and fiber and injection time on resin and fiber flow behavior were evaluated. Resin flow was unstable near the flow front but was stable after the flow front was passed. Regarding fiber flow, many fibers were oriented perpendicular to the flow direction in the mold width direction. On the other hand, fibers were randomly oriented in the mold thickness direction. The difference in orientation between the thickness direction and the width direction is considered to be caused by the resin injected from the injection nozzle spreading in the width direction while decreasing its velocity. These results are likely to be greatly affected by parameters such as mold shape and injection speed, and further evaluation of these parameters is needed. [ABSTRACT FROM AUTHOR]

In selective laser etching for the interfacial strengthening of Fiber Metal Laminates (FMLs), the wettability of customized structured features significantly affects their overall mechanical properties. For two-level features, their complicated geometry makes the evaluation of wettability difficult, which introduces challenges to feature design. In this study, a sandwich FMLs comprising an AA2024-O metal skin and a plain-woven carbon-fiber fabric core is investigated. Three types of two-level feature interfaces are designed and introduced into the FMLs structure. To understand the effect of two-level features on the resin flow and dynamic wetting progress in resin transfer molding, two-phase flow Computational Fluid Dynamics numerical models coupled with porous media and level-set numerical methods are built. The feasibility of the numerical simulation in the qualitative analysis was verified by comparing the results with those of the testing porosity values from X-ray scanning. In addition, the mechanisms of pore formation and transportation are revealed. These results indicate that the deeper groove structure in the etched features plays an important role in discharging residual air and determining the transformation of pores to reduce porosity. The permeability orientations of the porous media and surface structure both have significant impacts on the flow behavior and formation of pores. [ABSTRACT FROM AUTHOR]

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Compression resin transfer moulding (CRTM) has been widely used to manufacture automotive parts with reduced production cycle times. With the development of fast curing thermosetting resins, the CRTM process is a viable option for the high production rates in the transportation industry. However, the dynamic resin curing behaviour poses a potential risk of manufacturing defects in the part. In order to reduce the risk during the development of the tool and the process parameters, this paper proposes a modelling framework for the CRTM process when using fast curing resin systems. The work specifically focused on the coupling between heat transfer, resin cure, resin flow and preform compaction using a commercial code, PAM-RTM. The tool captures accurately the preform filling, temperature and resin pressure evolution during the injection and compression phase. The application of the framework was demonstrated for a complex 3D demonstrator. The predicted preform filling had an accuracy of 73% for the flow front evolution compared to the experimental results. This work demonstrates the validity of the framework proposed when dealing with resin systems that are challenging to process. [ABSTRACT FROM AUTHOR]

This numerical study aims to investigate the influence of I and T-shaped empty channels’ geometry on the filling time of resin in a rectangular porous enclosed mold, mimicking the main operating principle of a liquid resin infusion (LRI) process. Geometrical optimization was conducted with the constructal design (CD) and exhaustive search (ES) methods. The problem was subjected to two constraints (areas of porous mold and empty channels). In addition, the I and T-shaped channels were subjected to one and three degrees of freedom (DOF), respectively. Conservation equations of mass and momentum for modeling of resin/air mixture flow were numerically solved with the finite volume method (FVM). Interaction between the phases was considered with the volume of fluid method (VOF), and the effect of porous medium resistance in the resin flow was calculated with Darcy’s law. For the studied conditions, the best T-shaped configuration resulted in a filling time nearly three times lower than that for optimal I-shaped geometry, showing that the complexity of the channels benefited the performance. Moreover, the best T-shaped configurations were achieved for long single and bifurcated branches, except for configurations with skinny channels, which saw the generation of permanent voids.

Although various simulations have been conducted for the vacuum infusion process, most of the studies have considered only fabrics and flow medium and ignored the effect of peel ply. However, peel ply can affect resin flow because it is placed between the fabrics and flow medium. To verify this, permeability of two types of peel plies was measured, and it was found that the permeability between the peel plies differed significantly. Moreover, the permeability of the peel plies was lower than that of the carbon fabric; thus, peel ply can cause a bottleneck in the flow in the out-of-plane direction. Some 3D flow simulations were conducted in cases of no peel ply and for two types of the peel plies to confirm the effect of peel ply, and experiments were also conducted for two types of the peel plies. It was observed that filling time and flow pattern were highly dependent on the peel plies. The smaller permeability of peel ply has, the greater effect of peel ply is. These results indicate that the permeability of peel ply is one of the dominant factors and should be considered in process design in vacuum infusion. Additionally, by adding one layer of peel ply and applying permeability, the accuracy of flow simulations can be improved for filling time and pattern.

This article presents the numerical analysis and experimental investigation for the manufacturing of a foam core sandwich spoiler by vacuum-assisted resin injection (VARI) process. To find an injection scheme that guarantees both a good impregnation of the preform and a filling time compatible with the process window, the finite element model (FEM) was applied to analyze the effect of different injection schemes on the resin flow front patterns. Based on the obtained results, two optimal injection schemes are selected to form the spoiler structure. The experimental results show that the best molding quality can be achieved from the thick-end injection with a thin-end exit scheme. The comparison between simulation and experimental results shows that the overall deviation of the numerical analysis on resin flow time is 15.9%. [ABSTRACT FROM AUTHOR]

Since the existence of the complicated coupling between mould compression and resin flow, the full 3D simulation of the filling process in force-controlled compression resin transfer moulding (CRTM) has not been realized, especially when the resin flow front is irregular on the thickness direction during thick part moulding. In this paper, the coupled resin flow and mould compression behaviors are investigated firstly, a equivalent spring method is proposed to describe the preform compaction. The lubrication effect is taken into account, so the mould compression speed can be determined when the resin flow front is irregular on the thickness direction. Then the Volume of Fluid (VOF) two-phase model is established to express the resin-air flow in narrow gap and preform simultaneously, in which the narrow gap is considered as 3D area without flow resistance. Finally, the 3D numerical method for solving the above mathematical models is developed. In this method, the changing of the mould cavity is simulated by moving mesh technology, and the master-slave element method is used to simulate the resin squeezing from the infiltrated preform. Comparisons with analysis results are provided to prove the correctness of the above method, and two 3D examples are given to demonstrate the simulating capability.

This study proposes a method for predicting the impregnation time in the thickness direction of vacuum-assisted resin transfer molding (VaRTM) with flow media using 2D impregnation analysis and machine learning to reduce the computation time. We first let the machine learn the relationship between the impregnation time and the state of the flow media using a VaRTM 3D analysis. To predict the impregnation time in the thickness direction with low computation cost, a 2D analysis of the flow media portion was performed, and the impregnation time was predicted based on the 2D results and pre-trained machine learning program. For the prediction of a flat plate model with injection points that were different from the training model, the impregnation time error was 6.31%. Further, when using the machine learning model for the flat plate model, the analysis time was reduced to approximately 0.74% of the time required for the 3D analysis.