A potential repair alternative to restoring the mechanical properties of lightweight fiber-reinforced polymer (FRP) structures is to locally patch these areas with scarf joints. The effects of such repair methods on the structural integrity, however, are still largely unknown. In this paper, the mechanical property restoration, failure mechanism, and influence of fiber orientation mismatch between parent and repair materials of 1:50 scarf joints are studied on monolithic glass fiber-reinforced polymer (GFRP) specimens under tensile load. Two different parent orientations of [−45/+45]_2S and [0/90]_2S are exemplarily examined, and control specimens are taken as a baseline for the tensile strength and stiffness property recovery assessment. Using a layer-wise stress analysis with finite element simulations conducted with ANSYS Composite PrepPost to support the experimental investigation, the fiber orientation with respect to load direction is shown to affect the critical regions and thereby failure mechanism of the scarf joint specimens.

Two carbon fiber reinforced type IV pressure vessels are subjected to step-wise pressurization until burst, while monitored using acoustic emissions (AE). Unlike most prior studies, AE data is collected throughout the entire damage progression. The vessels, manufactured with differing parameters, failed in distinct composite layers – A-type in the hoop layers and B-type in the helical layers. The AE signals are evaluated to study material degradation and identify fiber breaks as signs of critical damage accumulation. The signals are distributed randomly across the surface, with localized accumulation only minutes before rupture, close to the rupture plane. The difference in manufacturing parameters did not result in any clear difference in the AE activity. Felicity and Shelby ratios show consistent decline with increasing pressure, suggesting potential for damage assessment and burst prediction. It is discussed how these ratios are affected by coupling quality of the AE sensors, the shape of the pressurization profile and prior loadings. Different signal features based on the amplitude and the frequency content are extracted for a classification into failure mechanisms. Based on previous studies, AE signals corresponding to fiber breaks have a characteristic high-frequency spectrum and show a delay in occurrence, with an increase in the number of breaking fibers towards the end of the experiment. Indeed, high frequency signals tend to occur later and signals in specific peak-frequency ranges (350 – 400 kHz, > 500 kHz) somewhat resemble the expected behavior. However, the dataset is too variable and too incongruent for any clear interpretations. Likely reasons are signal propagation effects, the complex composite structure, simultaneous occurrence of signals and measurement uncertainties. A review of relevant studies is provided to show that similar issues affect also previous works. Successfully identifying fiber breaks in large-scale, complex composite structures based on AE data, and turning this into an applicable health-monitoring technique, therefore remains a challenge.

The rapid development of artificial intelligence (AI) has significantly enhanced productivity, particularly in repetitive tasks. In the scientific domain, literature review stands out as a key area where AI-based tools can be effectively applied. This study presents a methodology for developing a search strategy for systematic reviews using AI tools. The Semantic Scholar database served as the foundation for the search process. The methodology was tested by searching for scientific papers related to batteries and hydrogen vehicles with the aim of enabling an evaluation for their potential applications. An extensive list of vehicles and their operational environments based on international standards and literature reviews was defined and used as the main input for the exemplary search. The AI-supported search yielded approximately 60,000 results, which were subjected to an initial relevance assessment. For the relevant papers, a neighbourhood analysis based on citation and reference networks was conducted. The final selection of papers, covering the period from 2013 to 2023, included 713 papers assessed after the initial review. An extensive discussion of the results is provided, including their categorisation based on search terms, publication years, and cluster analysis of powertrains, as well as operational environments of the vehicles involved. This case study illustrates the effectiveness of the proposed methodology and serves as a starting point for future research. The results demonstrate the potential of AI-based tools to enhance productivity when searching for scientific papers.

Hole-drilling method is a standardized technique for obtaining residual stresses in isotropic structures. Previous studies provide a foundation that enables the use of this method to investigate orthotropic structures, such as fiber-reinforced composites. In this study, the incremental hole-drilling method was applied to investigate residual stresses in filament wound type 4 composite pressure vessels. The investigated composite cylinders were manufactured with different internal pressure functions during the winding process, to achieve distinct residual stress states. Additionally, the influence of the initial loading under sustained internal pressure and increased temperature on the stress distribution was investigated. It was shown that the residual stress state can be influenced by varying the internal pressure in the winding process. After testing at sustained load and increased temperature, a stress redistribution was observed, which took place due to creep phenomena. Finally, a discussion of the challenges for the application of the hole-drilling method to composite pressure vessels is provided.