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

Abstract 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 R 2 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 R 2 value (0.9999) suggests that the model accurately captures the variance in impact force across varied impact conditions. Similarly, R 2 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.