X-ray computed tomography provides qualitative and quantitative structural and compositional information for a broad range of materials. Yet, its contribution to the field of advanced composites such as carbon fiber reinforced polymers is still limited by factors such as low imaging contrast, due to scarce X-ray attenuation features. This article, through a review of the state of the art, followed by an example case study on Micro-computed tomography (CT) analysis of low X-ray absorptive dry and prepreg carbon woven fabric composites, aims to highlight and address some challenges as well as best practices on performing scans that can capture key features of the material. In the case study, utilizing an Xradia Micro-CT-400, important aspects such as obtaining sufficient contrast, an examination of thin samples, sample size/resolution issues, and image-based modeling are discussed. The outcome of an optimized workflow in Micro-CT of composite fabrics can assist in further research efforts such as the generation of surface or volume meshes for the numerical modeling of underlying deformation mechanisms during their manufacturing processes.

The development and application of bio-sourced composites have been gaining wide attention, yet their deterioration due to the growth of ubiquitous microorganisms during storage/manufacturing/in-service phases is still not fully understood for optimum material selection and design purposes. In this study, samples of non-woven flax fibers, hemp fibers, and mats made of co-mingled randomly-oriented flax or hemp fiber (50%) and polypropylene fiber (50%) were subjected to 28 days of exposure to (i) no water-no fungi, (ii) water only and (iii) water along with the Chaetomium globosum fungus. Biocomposite samples were measured for weight loss over time, to observe the rate of fungal growth and the respiration of cellulose components in the fibers. Tensile testing was conducted to measure mechanical properties of the composite samples under different configurations. Scanning electron microscopy was employed to visualize fungal hyphal growth on the natural fibers, as well as to observe the fracture planes and failure modes of the biocomposite samples. Results showed that fungal growth significantly affects the dry mass as well as the tensile elastic modulus of the tested natural fiber mats and composites, and the effect depends on both the type and the length scale of fibers, as well as the exposure condition and time.

Biopolymer blends have attracted considerable attention in industrial applications due to their notable mechanical properties and biodegradability. This work delves into the innovative combination of butadiene-acrylonitrile (referred to as NBR) with a pectin-based biopolymer (NGP) at a 90:10 mass ratio through a detailed analysis employing mechanical characterization, Fourier transform infrared (FTIR) analysis, thermogravimetric analysis (TGA), and morphology studies using SEM. Additionally, biopolymer's biodegradability under aerobic and anaerobic conditions is tested. The study's findings underscore the superior tensile strength and elongation at break of the NGP/NBR blend in comparison to pure NBR, while also exhibiting a decrease in puncture resistance due to imperfect bonds at the particle-matrix interfaces, necessitating the use of a compatibilizer. In anaerobic conditions, evaluation of biodegradable properties reveals 2% and 12% biodegradability in NBR and NGP/NBR blend, respectively. The degradation properties were also aligned with TGA results highlighting a lower decomposition temperature for NGP. Additionally, this research integrates the application of a conditional value-at-risk (CVaR)-based analysis of the blend's tensile properties to evaluate the uncertainty impact in the experiment. Under risk, a significant enhancement in the tensile performance (by 80%) of the NGP/NBR blend was shown compared to pure NBR. Ultimately, the study shows that adding pectin to the NBR compound amplifies the overall performance of the biopolymer significantly under select criteria.

This study investigates the application of Bayesian Belief Networks (BBNs) to optimize thermoforming, an energy-intensive manufacturing process that rapidly shapes thermoplastic composite sheets into precise three-dimensional forms. Thermoforming involves complex interactions among process parameters, presenting ongoing challenges in achieving optimal energy efficiency, temperature stability, and consistent product quality. Further, the process can become highly non-linear with multiple sources of noise, due to the nature of the factory and logistical factors, adding further challenges to effectively predicting the process outcomes. To address this challenge, in this case study a Bayesian Belief Network was developed to capture the inherent uncertainties and intricate relationships among critical process variables. This probabilistic model integrated experimental observations, computational modeling results, and expert-derived insights, enabling a robust and adaptive decision-making support for the process designers. Each of the select key process parameters such as convective Heat Transfer Coefficient (HTC), and heating power, were prioritized based on their influence on the process performance indicators such as the end product quality, energy usage, temperature distribution error, settling time, and stability. Results demonstrated that the BBN framework provides an effective interactive decision support tool capable of continuous model refinements through updating of probabilities as new data became available, while achieving enhanced energy efficiency and process control, thereby also reducing the operational cost and material wastage.