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.

Over the last few decades, several marine renewable energy (MRE) technologies, such as wave energy converters (WECs) and current energy converters (CECs), have been developed. As opposed to traditional materials such as metal alloys, the structure of these technologies is made up of polymer and polymer composite materials. Most structures have been made using thermoset polymer composites; however, since thermoset polymer composites are not recyclable and lack sustainability, and with recent innovations in recyclable resins, bio-based resins, and the development of additive manufacturing technologies, thermoplastic polymers are increasingly being used. Nevertheless, the methodologies for identifying end-of-life options and recovering these polymer composites, as well as the recycling and reuse processes for MRE structures, are not well-studied. Specifically, since these MRE structures are subjected to salinity, moisture, varying temperature, biofouling, and corrosion effects depending on their usage, the recyclability after seawater aging and degradation needs to be explored. Hence, this review provides an in-depth review of polymer composites used in marine applications, the hygrothermal aging studies conducted so far to understand the degradation of these materials, and the reuse and recycling methodologies for end-of-life MRE structures, with a particular emphasis on sustainability.

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.

Molecular Dynamics for the Prediction of the Interfacial Shear Stress and Interface Dielectric Properties of Carbon Fiber Epoxy Composites