Fast recovery of non-fickian moisture absorption parameters for polymers and polymer composites

Guloglu, Gorkem E.; Hamidi, Youssef K.; Altan, M. Cengiz

doi:10.1002/pen.24469

Cited by 21 publications

(34 citation statements)

References 44 publications

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“…After the initial linear uptake, the absorption gradually slows down and eventually stops as saturation is reached [27]. However, most high-performance thermosetting polymers such as epoxy and bismaleimide resins exhibit a non-Fickian behavior [29][30][31][32][33]. Several models have been used to characterize this non-Fickian behavior, such as the coupled diffusion-relaxation [29], dual-diffusivity [30], and time-varying diffusion coefficient [31] models.…”

Section: Moisture Absorption Theorymentioning

confidence: 99%

“…Several models have been developed to characterize the moisture absorption behavior of polymers and their composites. These models range from the Fickian diffusion [27,28] to more sophisticated anomalous non-Fickian absorption models [29][30][31][32][33]. If the absorption dynamics of a composite is fully understood, then it would be possible to estimate variations in its properties due to the presence of moisture [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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Moisture Absorption of Carbon/Epoxy Nanocomposites

Guloglu

Altan

2020

J. Compos. Sci.

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Moisture absorption of composites with nanoscale carbon additives such as carbon nanotubes, carbon nanofibers, graphite nanoplatelets, and carbon black is investigated using thermogravimetric data and a non-Fickian hindered diffusion (Langmuir-type) model. The moisture absorption parameters are determined using this model for six different types of carbon/epoxy nanocomposites. The absorption behaviors obtained at different humidity levels and thermal environments are recovered by minimizing the error between the experimental data and model predictions, thus enabling the accurate determination of the moisture equilibrium level. The absorption behavior and the weight gain of all nanocomposites are shown to be accurately represented by this model over the entire absorption period. The presence of carbon nanomaterials is found to induce varying levels of non-Fickian behavior, governed by the nondimensional hindrance coefficient. This behavior is enhanced with the nanomaterial content and separate from the slight non-Fickian behavior of all neat epoxy samples. The molecular bonding during diffusion, as well as the interfacial moisture storage, could be among the reasons for non-Fickian behavior and should be included in the absorption models for accurate characterization of carbon/epoxy nanocomposites.

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Section: Moisture Absorption Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Moisture Absorption of Carbon/Epoxy Nanocomposites

Guloglu

Altan

2020

J. Compos. Sci.

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“…In addition, the presence of microcracks increases the composites vulnerability to moisture absorption and chemical attacks, ultimately reducing their durability and mechanical performance (Guloglu et al,2016;Mostafa et al, 2017). Microcracks can form during cure when the induced residual stresses are greater than the resin yield strength.…”

Section: Microcracksmentioning

confidence: 99%

Process Induced Defects in Liquid Molding Processes of Composites

Hamidi

Altan

2017

International Polymer Processing

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Liquid Composite Molding (LCM) processes are cost efficient manufacturing alternatives to traditional autoclave technology for producing near-net shape structural composite parts. However, process induced defects often limit wider usage of LCM in structural applications. Thorough knowledge of these defects, as well as their formation mechanisms and prevention techniques, is essential in developing improved LCM processes. In this article, process induced defects in liquid molding processes of composites, categorized into preform, flow induced and cure induced defects, are reviewed. Preform defects are further presented as fiber misalignment and fiber undulation (waviness and wrinkling). The respective causes, detrimental effects, and possible prevention methods of these defects are presented. Thereafter, flow induced defects are classified as voids and dry spots. Dry spot formation mechanisms in LCM processes and available prevention techniques are summarized. In addition, void formation mechanisms, adverse effects on composite properties, and removal techniques are presented. Cure induced defects include microcracks, void growth and geometrical distortions (warpage and spring-in). Each of these defects are discussed along with their underlying causes as well as their control and reduction schemes.

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“…Other phenomenological constitutive models were developed to predict different properties of composites by utilizing analytical expressions that describe the observed physical phenomena. For instance, the hindered diffusion model is known to accurately capture the moisture absorption behavior of a variety of polymer composites [2]. Nevertheless, these models usually depended on several empirical parameters that are recovered, using experimental data, and as such, are very specific to the studied composite type [2].…”

Section: Introductionmentioning

confidence: 99%

“…Void contents and manufacturing process are also found to influence significantly the moisture saturation levels for vinylester-based composites. This is expected since voids serve as storage locations for water molecules [2], and the manufacturing process would define, more or less, the level of void content in the fabricated composite [15]. What was not expected however, is that fiber content and conditioning environment and temperature did not seem to influence extensively the moisture saturation level of vinylester based composite laminates.…”

Section: Introductionmentioning

confidence: 99%

Prediction of moisture saturation levels for vinylester composite laminates: A data-driven approach for predicting the behavior of composite materials

Hamidi¹,

Berrado²,

Altan³

2019

AIP Conference Proceedings

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This paper introduces a comprehensive, data-driven method to predict the properties of composite materials, such as thermo-mechanical properties, moisture saturation level, durability, or other such important behavior. The approach is based on applying data mining techniques to the collective knowledge in the materials field. In this article, first, a comprehensive database is compiled from published research articles. Second, the Random Forests algorithm is used to build a predictive model that explains the investigated material response based on a wide variety of material and process variables (of different data types). This advanced statistical learning approach has the potential to drastically enhance the design of composite materials by selecting appropriate constituents and process parameters in order to optimize the response for a specific application. This method is demonstrated by predicting the moisture saturation level for vinylester-based composite laminates. Using 90% of the available published data available as the training dataset, the Random Forests algorithm is used to develop a regression model for the moisture saturation level. Variables considered by the model include the manufacturing process, the fiber type and architecture, the fiber and void contents, the matrix filler type and content, as well as the conditioning environment and temperature. On this training data, the model proved to be a good fit with a prediction accuracy of R 2 training=94.96%. When used to predict the moisture saturation level for the remaining unseen 10% of the compiled data, the model exhibited a prediction accuracy of R 2 test=85.28%. Furthermore, the Random Forests model allows the assessment of the impact of the different variables on the moisture saturation level. The fiber type is found to be the most important determinant on the moisture saturation level in vinylester composite laminates.

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Fast recovery of non-fickian moisture absorption parameters for polymers and polymer composites

Cited by 21 publications

References 44 publications

Moisture Absorption of Carbon/Epoxy Nanocomposites

Moisture Absorption of Carbon/Epoxy Nanocomposites

Process Induced Defects in Liquid Molding Processes of Composites

Prediction of moisture saturation levels for vinylester composite laminates: A data-driven approach for predicting the behavior of composite materials

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