Robust design of composites manufacturing processes with process simulation and optimisation methods

Li, J.; Zhang, C.; Liang, Richard; Wang, B.

doi:10.1080/00207540600806455

Cited by 17 publications

(16 citation statements)

References 10 publications

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“…Some of the important process parameters of the simulation model are fiber preform permeability and porosity, vacuum pressure, resin viscosity and resin injection gate and vent locations14. The gate location and vent location are treated as controllable factors.…”

Section: Performance Evaluation Of the Proposed Approachmentioning

confidence: 99%

“…In particular, the proposed method will be illustrated on a vacuum‐assisted resin transfer molding (VARTM) composite manufacturing process, widely used in fabricating large structures such as ship hulls and wind turbine blades. Computer simulation‐based modeling and optimization of resin transfer molding processes have been studied previously by Li et al 13, 14. Gou et al 15 employed fractional factorial experimental designs andfirst‐order polynomial response surface models to model and optimize a resin transfer molding process.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of a vacuum infusion process is to get the resin to its final destination as quickly as possible and to produce a part with low void content. The unbalance of resin flow during the filling process may cause voids or dry spots in the produced composite components, which are known as the largest sources of quality and repeatability problems14. Figure 1(a) shows a diagram of a typical VARTM set‐up17 and Figure 1(b) illustrates the application of the process for ship hull manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian approach for integration of physical and computer experiments for quality improvement in nano‐composite manufacturing

Vanli

Zhang

Chen

et al. 2010

Quality & Reliability Eng

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This paper presents a new Bayesian predictive approach for quality improvement in nano-composite manufacturing that aims to improve the accuracy of computer model simulations by combining physical process data and computer model predictions. Such a data augmentation method is of crucial importance for capital-intensive nano-composite processes for which large experimental studies are usually very costly or impractical. A simulation example and a case study on a real vacuum-assisted resin transfer molding (VARTM) composite manufacturing process are used to illustrate the proposed approach. The results of these studies show that the proposed approach can achieve more accurate predictions of the process variability than pure empirically based or pure simulation-based methods for a given experimental data size. An important practical implication of the results of this work is that, by using the proposed Bayesian method that leverages computational methods with the experimental data, one can accomplish significant reduction in product development costs by reducing the number of physical experiments and improving the prediction accuracy of computer models.

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Section: Performance Evaluation Of the Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bayesian approach for integration of physical and computer experiments for quality improvement in nano‐composite manufacturing

Vanli

Zhang

Chen

et al. 2010

Quality & Reliability Eng

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“…Therefore, advancements in numerical tools become the vital area of LCM research to optimize various process parameters. [10,11] The most commonly used numerical tools through various simulation packages are finite volume method (FVM), finite element method (FEM) and hybrid control volume finite element method (CVFEM). Moreover, the numerical tools used by researchers are specially developed simulation tools for the isothermal LCM processes such as PAM-RTM, RTMSIM, LIMS3D, LIMS, RTM-Worx, [12][13][14][15][16][17] etc.…”

Section: Introductionmentioning

confidence: 99%

Design of effective injection strategy and operable cure window for an aircraft wing flap composite part using neat resin characterization and multi‐physics process simulation

2022

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A methodology using neat resin characterization and multi‐physics process simulation was proposed to develop a resin transfer molding (RTM) process for an aircraft wing flap composite part. Initially, RTM6 resin was thermally characterized using differential scanning calorimetry and curing kinetics was modeled using Kamal and Sourour model. A multi‐physics approach was employed to simulate the RTM mold filling and curing phases. Mold filling simulations were performed to obtain an effective injection strategy for the composite part. One‐point injection port and five‐point vents were obtained as an effective injection strategy using a trial and improvement process. Curing simulations were performed on the composite part using neat resin cure kinetics and cure process windows for neat resin and composite panel were developed. Subsequently, a cure difference window was developed to investigate cure difference progression between neat resin and composite panel at the processing conditions. Finally, the kinetics of cure difference progression was modeled as a function of neat resin cure conversion to design operable cure cycles for the composite panel. The results found that the cure differences converge with the increase in applied mold temperature.

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“…The RTM process robustness is a broad research area, 10,11 many researchers have studied the issue of flow disturbances, especially the RT for its involvement in changing the flow pattern in a way that results in voids entrapment. Most of the researchers have proposed the use of sensors to predict the flow disturbances and some have proposed ways to control and correct the flow front pattern.…”

Section: Introductionmentioning

confidence: 99%

A new methodology for race-tracking detection and criticality in resin transfer molding process using pressure sensors

Siddig

Binétruy

Syerko

et al. 2018

Journal of Composite Materials

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In this study, a simplified cost effective simulation-based methodology is proposed to assist manufacturing engineers in the design and development phase of the resin transfer molding process. Race-tracking is unavoidable in the resin transfer molding and can lead to entrapment of air pockets, which results in parts being discarded as scrap. A purely numerical methodology is presented to distinguish between the critical and non-critical race-tracking scenarios, that will guide the design and production engineers plan an efficient and effective manufacturing strategy. The detection methodology is based on computing the pressure evolution with time during the injection process. The novelty relies on the superimposition of the computed pressure gradient maps that reveals unsuspected common features in the numerous race-tracking cases investigated in the various geometries of increasing complexity considered. The pressure sensors are meant to detect and evaluate different race-tracking scenarios and their level of criticality. The minimum number and locations of pressure sensors arise directly from the highest pressure gradient zones for simple geometries. Sensors placement guidelines are introduced for a simple rectangular shape, then this information is used to qualitatively apply the guidelines to parts of more complex shapes. A general rule that all parts, no matter how complex, can be considered as a combination of simpler ones is presented. Furthermore, a new failure criterion is proposed based on the flow patterns that highlights the likely flow patterns to entrap voids.

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Robust design of composites manufacturing processes with process simulation and optimisation methods

Cited by 17 publications

References 10 publications

A Bayesian approach for integration of physical and computer experiments for quality improvement in nano‐composite manufacturing

A Bayesian approach for integration of physical and computer experiments for quality improvement in nano‐composite manufacturing

Design of effective injection strategy and operable cure window for an aircraft wing flap composite part using neat resin characterization and multi‐physics process simulation

A new methodology for race-tracking detection and criticality in resin transfer molding process using pressure sensors

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