Kinetic analysis of the thermal decomposition of a carbon fibre-reinforced epoxy resin laminate

Tranchard, Pauline; Duquesne, Sophie; Samyn, Fabienne; Estèbe, Bruno; Bourbigot, Serge

doi:10.1016/j.jaap.2017.07.002

Cited by 89 publications

(61 citation statements)

References 25 publications

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“…Figure 11 shows the decomposition rate-temperature curves calculated at z = 1, 5, 10, 29 mm, respectively. It is similar to the typical DTG curves of polymer material at different rates of temperature increase [49], which is usually obtained from TGA and difficult to be derived in the practical coupons, elements and components. This capability in FE simulation is beneficial for comprehension on the decomposition process.…”

Section: Density and Decomposition Degreesupporting

confidence: 53%

“…Namely, at a certain temperature, the closer the position is to the heated surface, the lower the decomposition degree of the material at this position. This is because, when the same temperature or decomposition degree of material is achieved, the rates of temperature increase at different depths are different, while the pyrolysis reaction of phenolic resin is affected by the rate of temperature increase [49]. The smaller the rate of temperature increase, the more adequate the pyrolysis reaction of phenolic resin.…”

Section: Density and Decomposition Degreementioning

confidence: 99%

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Simulation of Thermal Behavior of Glass Fiber/Phenolic Composites Exposed to Heat Flux on One Side

Wang

Han

et al. 2020

Materials

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A 3D thermal response model is developed to evaluate the thermal behavior of glass fiber/phenolic composite exposed to heat flux on one side. The model is built upon heat transfer and energy conservation equations in which the heat transfer is in the form of anisotropic heat conduction, absorption by matrix decomposition, and diffusion of gas. Arrhenius equation is used to characterize the pyrolysis reaction of the materials. The diffusion equation for the decomposition gas is included for mass conservation. The temperature, density, decomposition degree, and rate are extracted to analyze the process of material decomposition, which is implemented by using the UMATHT (User subroutine to define a material’s thermal behavior) and USDFLD (User subroutine to redefine field variables) subroutines via ABAQUS code. By comparing the analysis results with experimental data, it is found that the model is valid to simulate the evolution of a glass fiber/phenolic composite exposed to heat flux from one side. The comparison also shows that longer time is taken to complete the pyrolysis reaction with increasing depth for materials from the numerical simulation, and the char region and the pyrolysis reaction region enlarge further with increasing time. Furthermore, the decomposition degree and temperature are correlated with depths, as well as the peak value of decomposition rate and the time to reach the peak value.

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Section: Density and Decomposition Degreesupporting

confidence: 53%

Section: Density and Decomposition Degreementioning

confidence: 99%

Simulation of Thermal Behavior of Glass Fiber/Phenolic Composites Exposed to Heat Flux on One Side

Wang

Han

et al. 2020

Materials

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“…The thickness of the sample was approximately 2.88 mm and the temperature ramped from 30 • C to 200 • C. The temperature range was initially chosen to be in the compatible zone with generic aerospace grade epoxy resin systems such as Hexcel HexPly®M21E/IMA [27][28][29]. M21E resin used for structural PMCs, has a complex formulation that includes three types of epoxy, a hardener, and two thermoplastic polymers and has been involved in a number of studies [30]. The curing properties are well documented, and this makes M21E an ideal benchmarking material for processing characterization procedures.…”

Section: Volume Changementioning

confidence: 99%

Elastomer Characterization Method for Trapped Rubber Processing

2020

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The increasing high-volume demand for polymer matrix composites (PMCs) brings into focus the need for autoclave alternative processing. Trapped rubber processing (TRP) of PMCs is a method capable of achieving high pressures during polymer matrix composite processing by utilizing thermally induced volume change of a nearly incompressible material inside a closed cavity mold. Recent advances in rubber materials and computational technology have made this processing technique more attractive. Elastomers can be doped with nanoparticles to increase thermal conductivity and this can be further tailored for local variations in thermal conductivity for TRP. In addition, recent advances in computer processing allow for simulation of coupled thermomechanical processes for full part modeling. This study presents a method of experimentally characterizing prospective rubber materials. The experiments are designed to characterize the dynamic in situ change in temperature, the dynamic change in volume, and the resulting real-time change in surface pressure. The material characterization is specifically designed to minimize the number and difficulty of experimental tests while fully capturing the rubber behavior for the TRP scenario. The experimental characterization was developed to provide the necessary data for accurate thermomechanical material models of nearly incompressible elastomeric polymers for use in TRP virtual design and optimization.Polymers 2020, 12, 686 2 of 13 based process design methodology. It is clear that numerical process models are required for TRP processing design. A well characterized rubber material model can then be used in conjunction with existing process modeling methods [10]. Recent advances in technology have made the TRP processing technique achievable for complex shapes and high-volume production. Extensive research in the computer electronics industry has developed a number of elastomers with high thermal conductivity. This increase in thermal conductivity is generally achieved by using nanoscale metallic additives [11].It is well known that the through-thickness degree of cure or crystallinity gradients cause non-thermoelastic residual stresses during PMC manufacturing [10]. Through-thickness cure gradients are exacerbated primarily by two mechanisms. One is the rate of thermal loading and the other is the thickness of the composite preform. High throughput automated PMC manufacturing can require high-temperature curing, but sharp distortions are intensified by increasing the processing temperature range [12,13]. In-plane residual stresses can be further intensified by increasing the thickness of the composite preform [14][15][16][17][18]. It is more efficient for thick parts, to processes the component in a single cycle, but typically multiple cycles are used to processes parts greater than the recommended thickness ranges due to the severity of cure gradient, residual stresses, and other phenomena [18]. Nano-additives can be exploited to customize the thermal conductivity of the TRP materi...

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“…In dependence on the properties of the investigated material and the test conditions, the thermogravimetry and differential thermogravimetry curves are often of a complicated, highly non-linear character. Application of different computational methods for their prediction with the use of appropriate mathematical models makes it possible to reduce the number of the ordinary laboratory procedures that are unavoidable for obtaining the experimental thermography /differential thermography data [15][16][17][18].…”

Section: Thermogravimetric Analysismentioning

confidence: 99%

Modeling the thermal decomposition of friction composite systems based on yarn reinforced polymer matrices using artificial neural networks

Kopal

Vršková

Ondrušová

et al. 2019

Materialwissenschaft Werkst

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The presented work deals with the application of artificial neural networks in the modelling of the thermal decomposition process of friction composite systems based on polymer matrices reinforced by yarns. The thermal decomposition of the automotive clutch friction composite system consisting of a polymer blend reinforced by yarns from organic, inorganic and metallic fibres impregnated with resin, as well as its individual components, was monitored by a method of non‐isothermal thermogravimetry over a wide temperature range. A supervised feed‐forward back‐propagation multi‐layer artificial neural network model, with temperature as the only input parameter, has been developed to predict the thermogravimetric curves of weight loss and time derivative of weight loss of studied friction composite system and its individual components acquired at a fixed constant heating rate under a pure dry nitrogen atmosphere at a constant flow rate. It has been proven that an optimized model with a 1‐25‐6 architecture of an artificial neural network trained by a Levenberg‐Marquardt algorithm is able to predict simultaneously all the analyzed experimental thermogravimetric curves with a high level of reliability and that it thus represents the highly effective artificial intelligence tool for the modelling of thermal stability also of relatively complicated friction composite systems.

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Kinetic analysis of the thermal decomposition of a carbon fibre-reinforced epoxy resin laminate

Cited by 89 publications

References 25 publications

Simulation of Thermal Behavior of Glass Fiber/Phenolic Composites Exposed to Heat Flux on One Side

Simulation of Thermal Behavior of Glass Fiber/Phenolic Composites Exposed to Heat Flux on One Side

Elastomer Characterization Method for Trapped Rubber Processing

Modeling the thermal decomposition of friction composite systems based on yarn reinforced polymer matrices using artificial neural networks

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