In the era of the 4th industrial revolution of big data, artificial intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous MS. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and prognostics health management of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for diagnosis, prognosis, and control.

In recent years, there has been a widespread growth in the application of composite materials particularly in the Aerospace and Automotive sectors. This is because composite structures are generally comparatively light in weight and provide corrosion and wear resistance as compared to metals or ceramics. Due to the strict fail-safe philosophy of the aerospace industry, the certification approach for current practice in joining composite materials is to thicken the joining areas and to use numerous fasteners which in turn increases the weight and stress concentrations in the structure. The use of adhesive bonding can improve the stress distribution between the composite materials / dissimilar materials and can contribute to a lighter structure. However, there much investigation is yet to be done in this discipline to predict the bond strength and performance using non-destructive evaluation methods. This paper will focus on an approach to study the mechanical as well as the dielectric properties of an adhesive bond. The dielectric testing is done by using Broadband Dielectric Spectroscopy (BbDS), wherein the dielectric characteristics of the material are analyzed in a wide frequency spectrum. The data obtained by this technique are used to demonstrate the charge transport, the combined dipolar fluctuation, and the effects of polarization occurring between the boundaries of materials. The continuous modifications of the dielectric spectra are due to the changes in the electrical and structural interactions between the particles, shapes, and orientations of the constituent phases of the morphological structure of the material system. Information about the morphologies, impurities/contamination or interaction of the dissimilar surfaces of the pristine bond can be obtained from the initial BbDS properties. The dielectric properties for adhesively bonded composites with different surface adhesion properties have shown promising evidence of predicting the final mechanical performance of the bonded material system. The success and limitations of this approach will be discussed, and needs for continued investigation identified

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.

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Composite materials are essential for many modern applications, including airplanes and cars, energy conversion and storage devices, medical prosthetics, and civil structures. Detecting the initiation, growth, accumulation, and coalescence of micro-damage in these heterogeneous materials and predicting the onset of component failure using conformal Broadband Dielectric Spectroscopy (BbDS) is a promising area of ongoing research. Recently, the authors have developed the critical path concept and Heterogeneous fracture mechanics concept that depicts the effect of defect nucleation, growth, coalescence, and fracture plane development and correlation of these damage mechanisms to change in dielectric response respectively. Current research applies those concepts to detect the damage mode and the critical fracture path (conduction path) that leads to eventual failure. Also, the use of BBDS to detect the weak adhesion bonding is proposed, where the change in dielectric properties as a function of the frequency of the applied potential through the thickness of the sample is measured in the vicinity of the weak region. It is observed that at low frequency the gradient in the potential indicates a spike which marks the charge concentration around the imperfect region.