Desirable features in mold filling simulations for Liquid Composite Molding processes

Šimáček, Pavel; Advani, Suresh G.

doi:10.1002/pc.20029

Cited by 149 publications

(17 citation statements)

References 21 publications

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“…Mathematically, this is done by prescribing the cost function that will minimize the region with no resin in the mold. Evaluation of the cost function is performed with an existing numerical simulation called Liquid Injection Molding Simulation (LIMS) [30] which simulates the flow in any complex geometry in Liquid Molding. LIMS output provides the empty region after each fill based on the inputs of preform and DM permeability, race-tracking strengths along the edges and predefined inlet and vent locations.…”

Section: Methodology and Implementationmentioning

confidence: 99%

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A methodology to reduce variability during vacuum infusion with optimized design of distribution media

Şaş

Šimáček

Advani

2015

Composites Part A: Applied Science and Manufacturing

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a b s t r a c t Seemann Composites Resin Infusion Molding Process (SCRIMP) is a widely used version of VacuumAssisted Resin Transfer Molding (VARTM) in which a highly permeable layer (distribution media) is placed on top of the dry preform to distribute the resin with very low flow resistance to reduce the filling and hence the manufacturing time. The flow patterns during filling may vary from part to part due to the variability associated with the material, part geometry, and layup of the assembly, which may result in race-tracking channels. The process is considered as reliable and robust only if the resin completely saturates the preform despite changing filling patterns caused by flow disturbances.The resin flow pattern can be manipulated with a tailored distribution media layout as it does impact the flow patterns significantly. The continuous distribution media layout over the entire part surface works well for very simple geometries with no to little potential for race-tracking along the edges. In this study we address complex cases, which require placement of an insert within the assembly, which will introduce race-tracking along its edges, and hence uniform placement of distribution media over the entire top surface will fail to yield a void free part. We introduce a methodology using a predictive tool to design an optimal shape of distribution media, which accounts for the flow variability introduced due to race-tracking along the edges of the inserts. This iterative approach quickly converges to provide the placement of distribution media on selective areas of the preform surface that ensures complete filling of the preform despite the variability. This approach has been validated with an experimental example and will help mitigate risk involved in manufacturing complex composites components with Liquid Molding.

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Section: Methodology and Implementationmentioning

confidence: 99%

“…6 to find the DM layout to be placed on top of the preform so no voids are created [30]. The mesh representing the preform domain is shown in Fig.…”

Section: Experimental Validationmentioning

confidence: 99%

A methodology to reduce variability during vacuum infusion with optimized design of distribution media

Şaş

Šimáček

Advani

2015

Composites Part A: Applied Science and Manufacturing

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“…The scientific investigations [2,13,14] carried out over the last 50 years on LCM techniques have provided experimental, analytical, and numerical methods to investigate mold filling for different LCM processes. Various software packages are nowadays available to carry out numerical simulations [15][16][17], which assist in defining an effective tool configuration, namely positioning inlet gates and vents. Different filling strategies and manufacturing parameters (injection pressure or flow rate, resin and mold temperatures, etc.)…”

Section: State Of the Artmentioning

confidence: 99%

A Dimensionless Characteristic Number for Process Selection and Mold Design in Composites Manufacturing: Part I—Theory

et al. 2020

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The present article introduces a dimensionless number devised to assist composite engineers in the fabrication of continuous fiber composites by Liquid Composite Molding (LCM), i.e., by injecting a liquid polymer resin through a fibrous reinforcement contained in a closed mold. This dimensionless number is calculated by integrating the ratio of the injection pressure to the liquid viscosity over the cavity filling time. It is hereby called the “injectability number” and provides an evaluation of the difficulty to inject a liquid into a porous material for a given part geometry, permeability distribution, and position of the inlet gate. The theoretical aspects behind this new concept are analyzed in Part I of the article, which demonstrates the invariance of the injectability number with respect to process parameters like constant and varying injection pressure or flow rate. Part I also details how process engineers can use the injectability number to address challenges in composite fabrication, such as process selection, mold design, and parameter optimization. Thanks to the injectability number, the optimal position of the inlet gate can be assessed and injection parameters scaled to speed up mold design. Part II of the article completes the demonstration of the novel concept by applying it to a series of LCM process examples of increasing complexity.

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“…Composite engineers face three main challenges during Liquid Composite Molding (LCM) process development: comparing different injection processes and identifying the most suitable one (Challenge 1); finding an appropriate mold configuration (Challenge 2); and setting optimal values of process parameters (Challenge 3). The technical literature reports several methods to investigate mold filling in LCM [1][2][3][4], and various software packages have been developed to simulate resin injection for process design [5][6][7][8]. However, in common industrial practice, decisions on process feasibility and mold design are largely based on experience or made by trial and error.…”

Section: Introductionmentioning

confidence: 99%

A Dimensionless Characteristic Number for Process Selection and Mold Design in Composites Manufacturing: Part II—Applications

et al. 2020

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The dimensionless “injectability number” was devised to assist composite engineers in the fabrication of continuous fiber composites by Liquid Composite Molding (LCM), i.e., by injecting a liquid polymer resin through a fibrous reinforcement contained in a mold cavity. Part I of this article introduced the injectability number as the integral of the ratio of the injection pressure to the resin viscosity over the cavity filling time and analyzed the theoretical aspects behind this new concept. For a given mold configuration and reinforcement material characteristics, the invariance of the injectability number with regard to process parameters was demonstrated, and an initial verification in unidirectional injection cases was conducted. Part II completes the analysis by evaluating the injectability number in more complex application cases, confirming its invariance properties. The investigation, which was carried out using numerical simulations of different LCM processes and injection strategies, examined the fabrication of various composite parts: a rectangular laminate, a hood for automotive applications, a reservoir box and a fuselage section for the aerospace industry. The results indicate that more efficient injection strategies lead to lower values of the injectability number, thus enabling the use of this dimensionless number as a tool to assess the difficulty to manufacture a given part by LCM as well as to guide process selection and compare different mold configurations.

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Desirable features in mold filling simulations for Liquid Composite Molding processes

Cited by 149 publications

References 21 publications

A methodology to reduce variability during vacuum infusion with optimized design of distribution media

A methodology to reduce variability during vacuum infusion with optimized design of distribution media

A Dimensionless Characteristic Number for Process Selection and Mold Design in Composites Manufacturing: Part I—Theory

A Dimensionless Characteristic Number for Process Selection and Mold Design in Composites Manufacturing: Part II—Applications

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