Dielectric state variables as qualitative indicators of moisture absorption-caused mechanical property degradation in GFRP composite structures

Das, Partha Pratim; Vadlamudi, Vamsee; Raihan, Rassel

doi:10.1016/j.jcomc.2022.100295

Cited by 5 publications

(7 citation statements)

References 45 publications

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“…This can be attributed to the dipolar polarization that is observable near 1000 Hz. This effect is a direct indicator of the moisture absorption phenomenon, as reported in the literature [ 16 , 45 ].…”

Section: Model Development and Discussionmentioning

confidence: 62%

“… Heatmap of normalized real permittivity and trend of moisture absorption with immersion time [ 16 ]. …”

Section: Figurementioning

confidence: 99%

“…Other methods, including differential scanning calorimetry (DSC), Fourier transform infrared (FTIR), etc., have also been utilized in composite structures to detect the presence of moisture in composites [ 15 ]. However, these all have their limitations [ 16 ]. For instance, DSC tests require a very small size of samples that may not represent the actual moisture state of a composite, and this is not the non-destructive testing (NDT) method.…”

Section: Introductionmentioning

confidence: 99%

“…Das et al used BBDS to qualitatively characterize the moisture absorption-caused mechanical strength degradation in GFRP composites [ 16 ]. Figure 3 summarizes the results of the referenced work, where it is evident that the change in real permittivity strongly correlates with absorbed moisture with immersion time.…”

Section: Introductionmentioning

confidence: 99%

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Moisture Content Prediction in Polymer Composites Using Machine Learning Techniques

et al. 2022

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The principal objective of this study is to employ non-destructive broadband dielectric spectroscopy/impedance spectroscopy and machine learning techniques to estimate the moisture content in FRP composites under hygrothermal aging. Here, classification and regression machine learning models that can accurately predict the current moisture saturation state are developed using the frequency domain dielectric response of the composite, in conjunction with the time domain hygrothermal aging effect. First, to categorize the composites based on the present state of the absorbed moisture supervised classification learning models (i.e., quadratic discriminant analysis (QDA), support vector machine (SVM), and artificial neural network-based multilayer perceptron (MLP) classifier) have been developed. Later, to accurately estimate the relative moisture absorption from the dielectric data, supervised regression models (i.e., multiple linear regression (MLR), decision tree regression (DTR), and multi-layer perceptron (MLP) regression) have been developed, which can effectively estimate the relative moisture absorption from the dielectric response of the material with an R2 value greater than 0.95. The physics behind the hygrothermal aging of the composites has then been interpreted by comparing the model attributes to see which characteristics most strongly influence the predictions.

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Section: Model Development and Discussionmentioning

confidence: 62%

“… Heatmap of normalized real permittivity and trend of moisture absorption with immersion time [ 16 ]. …”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moisture Content Prediction in Polymer Composites Using Machine Learning Techniques

et al. 2022

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“…In this study, broadband DS was used to monitor the dielectric behavior of glass-FRPC samples immersed in water for varying durations. 81 The real permittivity of the samples increased with increasing moisture intake and immersion time across all frequencies. This result was due to the different dielectric polarizations induced by absorbed moisture.…”

Section: Ds Of Fiber-reinforced Polymer Compositesmentioning

confidence: 93%

Dielectric characterization of fiber‐ and nanofiller‐reinforced polymeric materials

Quader,

Narayanan,

Caldona

2024

J of Applied Polymer Sci

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This review provides an in‐depth analysis of the electrical responses and relaxation behaviors of various polymeric materials including fiber‐reinforced polymer composites, polymer nanocomposites (PNC), and nanofiller‐reinforced polymeric films and coatings using dielectric spectroscopy (DS). The underlying theory of DS, corresponding instrumentation, various dielectric parameters, relevant equations, interpretations, and polarization mechanisms are thoroughly explored. The impact of synthetic and natural fibers and a diverse range of nanofillers on the dielectric responses and relaxation mechanisms of fiber‐reinforced composites, and PNC and films, respectively, are also examined. For each summarized article, we provide an overview of the composite or film preparation, followed by concise explanations of the dielectric behaviors under diverse conditions, including variations in temperature, frequency, moisture levels, fiber/filler ratios, and chemical treatment. In essence, this review contributes to a nuanced understanding of the intricate relationship between material structure, properties, and performance via DS data interpretation across a spectrum of scenarios.

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Dielectric and Mechanical Response of Cold-Curing Epoxy Resin to Hygrothermal Stress

Leffler,

Slama,

Mokra

et al. 2024

2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika)

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Dielectric state variables as qualitative indicators of moisture absorption-caused mechanical property degradation in GFRP composite structures

Cited by 5 publications

References 45 publications

Moisture Content Prediction in Polymer Composites Using Machine Learning Techniques

Moisture Content Prediction in Polymer Composites Using Machine Learning Techniques

Dielectric characterization of fiber‐ and nanofiller‐reinforced polymeric materials

Dielectric and Mechanical Response of Cold-Curing Epoxy Resin to Hygrothermal Stress

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