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.

This research work focuses on the development of a piezoelectric magnetostrictive smart composite with advanced sensing capability. The composite piezoelectric property is achieved from the dispersion of single-walled carbon nanotubes (SWCNTs) and the magnetostrictive property from Terfenol-D nanoparticles. Finite element analysis (FEA) is used to examine the feasibility of modelling the piezoelectric (change in electric field) and magnetostrictive (change in magnetic field) self-sensing responses in the presence of applied stress. The numerical work was coupled with a series of mechanical tests to characterize the piezoelectric response, magnetostriction response and mechanical strength. Tensile tests of the composite samples manufactured as is (virgin), samples with SWCNTs, samples with Terfenol-D nanoparticles and samples with both SWCNTs and Terfenol-D nanoparticles were conducted. It was observed that an increase in volume fraction of Terfenol-d nanoparticles increases the change in magnetization, therefore increasing voltage response up to the point of saturation. The optimum change in amplitude was observed with 0.35% volume fraction of Terfenol-D nanoparticles. A constant ratio of SWCNTs was maintained, and maximum change in electrical resistance was at 7.4%. Fracture toughness for the samples with all nanoparticles was explored, and the results showed improved resistance to crack propagation.

The aviation and automobile industries have recently depended on thermoset epoxy-based prepreg as a raw material for manufacturing composites. Since prepregs have a limited out-life (i.e., the maximum storing time allowed at room temperature), they must be stored in refrigerators at low temperatures. If not maintained, they can often adversely affect the desired quality of the final product. Prepregs are often discarded once the prepreg out-life ends, leading to a loss of millions of dollars and a detrimental impact on the environment. Therefore, it is necessary to develop a simple method to monitor prepreg aging/degradation in order to ensure its usability or repurpose prepreg usage. In this study, we used broadband dielectric spectroscopy to monitor the degradation state of the glass fiber/epoxy prepreg sample stored at room temperature, and the data has been used to predict the mechanical performance of the final manufactured composite part. The frequency-based nondestructive dielectric measuring technique was used to calculate the dielectric strength and relaxation time of the prepregs. The dielectric strength decreased and relaxation time increased with aging, allowing the aging progression to be captured. Furthermore, with aging, the real permittivity value changes to a lower value and the relaxation peak in imaginary permittivity with respect to frequency shifts to a lower frequency. The physics behind this dielectric measurement method has been understood in the context of analyzing the prepreg’s cure kinetics parameters, which has been investigated using differential scanning calorimetry. Overall, this simple dielectric-based monitoring technology will provide confidence in the future use of degraded or out-of-life raw material systems for manufacturing.

Thermal conductive gap filler materials are used as thermal interface materials (TIMs) in electronic devices due their numerous advantages, such as higher thermal conductivity, ease of use, and conformity. Silicone is a class of synthetic materials based on a polymeric siloxane backbone which is widely used in thermal gap filler materials. In electronic packages, silicone-based thermal gap filler materials are widely used in industries, whereas silicone-free thermal gap filler materials are emerging as new alternatives for numerous electronics applications. Certainly, characterization of these TIMs is of immense importance since it plays a critical role in heat dissipation and long-term reliability of the electronic packages. Insubstantial studies on the effects of various chemical compounds on the properties of silicone-based and silicone-free TIMs has led to this study, which focuses on the effect of thermal aging on the mechanical, thermal, and dielectric properties of silicone-based and silicone-free TIMs and the chemical compounds that cause the changes in properties of these materials. Characterization techniques such as dynamic mechanical analysis (DMA), thermomechanical analysis (TMA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and broadband dielectric spectroscopy (BbDS) are used to study the mechanical, thermal, and dielectric characteristics of these TIMs, which will guide towards a better understanding of the applicability and reliability of these TIMs. The experiments demonstrate that upon thermal aging at 125 °C, the silicone-free TIM becomes hard, while silicone-based TIM remains viscoelastic, which indicates its wide applicability to higher temperature applications for a long time. Though silicone-based TIM displays better mechanical and thermal properties at elevated temperatures, dielectric properties indicate low conductivity for silicone-free TIM, which makes it a better candidate for silicone-sensitive applications where higher electric insulation is desired.

The use of fiber-reinforced composite materials has widely spread in various sectors, including aerospace, defense, and civil industry. The assessment of these heterogeneous material systems is important for safer and risk-free applications and has contributed to the need for self-sensing composites. This work is focused on the development of piezoresistive composites, the prediction of their performance and structural health monitoring (SHM). Additionally, this work unpacks the complexity of carbon nanotubes (CNTs) micro-fabrication and the development of piezoresistive and electromagnetic (EM) waves detection electrodes. Scanning electron microscopy (SEM) was used to characterize the CNTs structure and morphologies. The manufactured CNTs were incorporated in epoxy systems to fabricate glass fiber reinforced polymer (GFRP)-CNTs smart composites with piezoresistive properties. The detection of micro-damage onset and its progression was carried out in mode I, to evaluate the sensitivity of the smart composites to damage development. The change in electrical conductivity of the nanotubes-reinforced composite systems due to localized mechanical strains enabled crack propagation detection. The relationship between crack propagation, fracture toughness, and electrical resistivity of the smart composite was analyzed.