Effect of material, process, and equipment variables on the performance of resin transfer moulded parts

Karbhari, Vistasp M.; Slotte, Stefan G.; Steenkamer, D. A.; Wilkins, Dick J.

doi:10.1016/0956-7143(92)90077-8

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…Shrinkage behavior of plastic parts is related to many factors such as processing equipment, material properties, mold structure, plastic parts geometry, and injection molding process parameters [ 29 , 30 , 31 ]. For gears whose dimensions have been determined in practical production, the key concern is the influence of injection molding process parameters on shrinkage.…”

Section: Introductionmentioning

confidence: 99%

A Practical Numerical Approach to Characterizing Non-Linear Shrinkage and Optimizing Dimensional Deviation of Injection-Molded Small Module Plastic Gears

2021

Polymers

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This paper was aimed at finding out the solution to the problem of insufficient dimensional accuracy caused by non-linear shrinkage deformation during injection molding of small module plastic gears. A practical numerical approach was proposed to characterize the non-linear shrinkage and optimize the dimensional deviation of the small module plastic gears. Specifically, Moldflow analysis was applied to visually simulate the shrinkage process of small module plastic gears during injection molding. A 3D shrinkage gear model was obtained and exported to compare with the designed gear model. After analyzing the non-linear shrinkage characteristics, the dimensional deviation of the addendum circle diameter and root circle diameter was investigated by orthogonal experiments. In the end, a high-speed cooling concept for the mold plate and the gear cavity was proposed to optimize the dimensional deviation. It was confirmed that the cooling rate is the most influential factor on the non-linear shrinkage of the injection-molded small module plastic gears. The dimensional deviation of the addendum circle diameter and the root circle diameter can be reduced by 22.79% and 22.99% with the proposed high-speed cooling concept, respectively.

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Section: Introductionmentioning

confidence: 99%

A Practical Numerical Approach to Characterizing Non-Linear Shrinkage and Optimizing Dimensional Deviation of Injection-Molded Small Module Plastic Gears

2021

Polymers

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“…In RTM, there are several variables associated with the reliability and repeatability of the process and the quality of the final part [1]. At the fabric level, intricate fiber architectures can constitute a challenge when draping over multi-directional faces in a mold.…”

Section: Introductionmentioning

confidence: 99%

“…L IQUID MOLDING TECHNIQUES, such as Resin Transfer Molding (RTM), are being used in the automotive and aerospace industries due to their ability to produce structural composites. In RTM, there are several variables associated with the reliability and repeatability of the process and the quality of the final part [1]. At the fabric level, intricate fiber architectures can constitute a challenge when draping over multi-directional faces in a mold.…”

Section: Introductionmentioning

confidence: 99%

Experimental Characterization of the Influence of Tackifier Material on Preform Permeability

Estrada¹,

Vieux-Pernon²,

Advani

2002

Journal of Composite Materials

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Liquid composite molding processes use both simple and complex sets of preforms to produce high-performance composite parts. To produce these parts, binder materials known as tackifiers are used to shape the fabrics into complicated preforms prior to the resin injection. Tackifier materials enhance the formability and handling operations of dry reinforcements, and become an integral part of the composite laminate. A commercial tackifier was employed to form fiberglass preforms. Various preforms were prepared with different concentrations of the tackifier, and the effect on the permeability was studied. A permeability work-cell was used to continuously measure the permeability of the tackified preform for different fiber volume fractions. An analytical model based on a unit cell definition in combination with microscopic images was used to predict the permeability of tackified preforms. A comparison between the experimental measurement and the predicted results show the deficiencies in the unit cell approach to model permeability of preforms containing low concentrations of tackifiers.

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“…At the same injection pressures, the respective composites had maximum flexural strengths of 87.36, 120.73 and 151.23 MPa. Flexural strength also decreased beyond the optimal pressure due to increase of void content present in the composite as noticed by Karbhari et al [34]. The composites also had maximum impact strengths of 351.58, 435.94 and 468.19 kJ/m 2 .…”

Section: Analysis Of Variancementioning

confidence: 54%

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

Kopparthi

Kundavarapu

Dasari³

et al. 2019

SN Appl. Sci.

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The present work is aimed at determining mechanical properties of chopped strand glass fiber reinforced composite laminates manufactured based on the design of experiments by resin transfer molding at various injection pressures with 4, 5 and 6 layers. Response surface methodology was implemented to the experimental data for evaluating the effect of number of layers and resin injection pressure on mechanical properties and void content. Teaching learning based optimization (TLBO) has been proposed to predict optimal (maximum) mechanical properties of composite by optimizing the number of layers and injection pressure. Artificial neural network (ANN) with feed forward back propagation algorithm was also used to predict the responses and compare with experimental and TLBO results. It was found that the predicted values of responses from TLBO and ANN are good in agreement with experimental results.Keywords Artificial neural network (ANN) · Teaching learning based optimization (TLBO) · Glass fiber reinforced plastic (GFRP) · Resin transfer molding (RTM) · Mechanical properties

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Effect of material, process, and equipment variables on the performance of resin transfer moulded parts

Cited by 14 publications

References 3 publications

A Practical Numerical Approach to Characterizing Non-Linear Shrinkage and Optimizing Dimensional Deviation of Injection-Molded Small Module Plastic Gears

A Practical Numerical Approach to Characterizing Non-Linear Shrinkage and Optimizing Dimensional Deviation of Injection-Molded Small Module Plastic Gears

Experimental Characterization of the Influence of Tackifier Material on Preform Permeability

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

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