Enhancing mechanical properties of GFRP–aluminum joints through Z pinning: a low velocity shear impact study

Assessing the mechanical properties of CFRP and aluminum specimens exposed to hygrothermal aging is vital. Moreover, it is important to develop strategies to improve these properties. This study examines the influence of fullerene and Single‐Walled Carbon Nanotubes (SWCNT) on the fatigue life and static strength of bonded and bonded/bolted joints. The research concentrates on composite‐to‐composite and composite‐to‐aluminum substrates under three‐point bending tests, both prior to and after hygrothermal aging. The samples were classified into four groups: (1) neat specimens, (2) specimens with added fullerene, (3) specimens containing SWCNT, and (4) specimens with a blend of 50% SWCNT and 50% fullerene.The findings indicated that the optimal nanoparticle ratio for bonded joints differs from that for bonded/bolted joints. Incorporating nanoparticles into the adhesive enhanced the fatigue life of single lap joints (SLJs), particularly in samples with mixed particles and SWCNT. In some instances, nanoparticles intensified the effects of hygrothermal conditions, further increasing fatigue life. The incorporation of nanoparticles and the use of bonded/bolted joints significantly enhanced joint strength, with the combination of both yielding the best results. This study improves the understanding of aging in adhesive and hybrid joints, particularly in dissimilar configurations, and offers insights into their performance under various environmental conditions.Highlights Study examines fullerene and SWCNT impacts on CTC/CTA joint strength and fatigue. Optimal nanoparticle ratios differ for bonded and bonded/bolted joints. Nanoparticles reduce moisture absorption, aging damage, and increase failure load. Nanoparticles enhance fatigue life, varying by type, volume, load, and joint. Incorporating nanoparticles significantly improves joint strength.

Investigating the durability of nano‐reinforced CFRP‐aluminum and CFRP‐CFRP bonded and bonded/bolted joints under hygrothermal conditions

Karimi

2024

Utilizing a combination of experimental and machine learning methods to predict and correlate between accelerated and natural aging of CFRP/AL adhesive joints under hygrothermal conditions

Karimi,

2024

This study investigates how carbon fiber reinforced polymer (CFRP)‐to‐aluminum adhesive joints behave under accelerated aging conditions with hygrothermal exposure and compares these findings against naturally aged samples to evaluate material reliability in challenging environments. The CFRP‐to‐aluminum adhesive joints were manufactured and subjected to natural aging for durations ranging from 1 to 3 years with 6‐month intervals, as well as accelerated aging (hygrothermal) for periods ranging from 100 to 1200 h, with intervals of 50 h. Subsequently, the mechanical properties of the joints were evaluated using a three‐point bending test. To forecast natural aging times from accelerated aging data, five machine learning models were utilized: artificial neural network, support vector regression, linear regression, polynomial regression, and random forest regression. Hygrothermal aging significantly degraded the matrix, causing a shift in failure modes from cohesive to mixed types (cohesive, adhesive, and fiber tear failures), leading to a notable decline in bending strength. The study observed a 23.13% strength reduction in samples aged naturally for 3 years and a 24.33% decrease in those subjected to 1000 h of accelerated aging. The random forest regressor demonstrated superior accuracy in predicting natural aging times across different accelerated aging periods. Through the application of machine learning models, this study introduces a novel approach to forecast natural aging durations using data from accelerated aging experiments. This method shows potential for optimizing joints and composite structures, ultimately improving their durability and minimizing the likelihood of failures during operational use.Highlights Studied hygrothermal effects on accelerated aging of carbon fiber reinforced polymer/Aluminum (AL) adhesive joints. Noted strength reduction from hygrothermal aging. Used five machine learning models; random forest regression had the highest accuracy. Analyzed correlation between natural and accelerated aging of dissimilar adhesive joints.

Impact behavior analysis of carbon fiber‐reinforced polymer composites via a data‐driven scheme with Artificial Neural Network approach

Hiremath,

Jung,

et al. 2024

In this study, the impact performance of carbon fiber‐reinforced polymer (CFRP) composites under low‐speed impact conditions was investigated using a data‐driven approach. Both material properties and impact parameters were determined through experimental methods and finite element (FE) analysis. FE analysis was conducted on CFRP composite structures to generate impact force and absorbed energy datasets. Various impact conditions, such as impactor height (0.5–1.25 m), impactor shape (flat, truncated cone, bullet, and cone), and composite plate thickness (1–4 mm), were incorporated into an artificial neural network (ANN) model to predict the impact behavior of CFRP composites. Using the optimal plate thickness identified from the data‐driven model, CFRP plates were fabricated using vacuum‐assisted resin transfer molding and tested under different impactor shapes and heights using drop impactors. The FE analysis revealed that increasing the impactor height improved the impact force by 37.8% and the absorbed energy by 178%. The impactor shape also significantly influenced the results, with a flat‐to‐cone‐shaped impactor increasing the impact force and absorbed energy by 167.2% and 440%, respectively. Additionally, the plate thickness analysis showed that a 2 mm plate provided optimal impact force and absorbed energy, with values of 1.09 kN and 8.12 J, respectively. The prediction of the force and energy experienced by CFRP material under different impact conditions was validated using root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE), and R2 metrics. The model demonstrated excellent performance with the lowest RMSE (0.0118), MSE (0.0003), and MAE (0.0096), indicating that the predicted impact forces closely matched the actual forces. The highest R2 value (0.9999) suggests that the model accurately captures the variance in impact force across varied impact conditions. Similarly, R2 values close to one indicate that the model effectively explains the variability in energy absorption, making it highly reliable. The ANN model also showed that predictions for absorbed energy were more accurate than those for impact force under varying impact conditions. Furthermore, the predicted impact force and absorbed energy from the FE analysis and ANN model closely aligned with the experimental results. Damage morphology observations indicated matrix cracking at higher impact velocities, with more significant penetration occurring with cone‐shaped impactors. These findings demonstrate a strong correlation between experimental and numerical outcomes, validating the effectiveness of this combined approach for evaluating the impact resistance of CFRP composites.Highlights The impact performance of CFRP composites under different impact conditions was modeled. An Artificial Neural Network model was developed to predict impact performance. The VARTM method was adopted for the development of optimized CFRP composites. Impactor height and shape were found to be the most influential factors Impact damage areas were related based on impactor height and shape.