Bumper Impact Test Damage and Static Structural Characterization in Carbon-Epoxy Composites Aided by Numerical Simulation and Machine Learning Analysis

Modern automotive design has increasingly embraced plastics for bumper construction, however it can lead to material degradation. To overcome these limitations, the automotive industry is turning to fiber‑resin material, namely carbon-epoxy composites. Our research focuses on knowing layer thickness and orientation angles effect into structural stress of car bumper, examining the combinations of material into the impact strength, and performing impact tests in various speeds. To build research gaps, machine learning (ML) has an important position to predict the certain value of parameters and measure data normality through various algorithms. By combining ML and FEA simulations, the result shows strong data performance. Bridging from 3 mm mesh sizing of 100% carbon-epoxy woven laminate in 6 mm thickness at 0° orientation shows the most distributed von Mises stresses which converged toward stable values. The elevated impact velocities improve stress magnitudes at 10.247 MPa, 16.857 MPa, and 23.438 MPa in 11.11 m/s, 15.61 m/s, and 22.22 m/s, respectively. For comprehensive research, total deformation was included in ML analysis as second targets to build multivariate analysis. Overall, Random Forest (RF) shows as the best model which indicates MSE (0.18) and R² (0.79), indicating superior robustness when modeling equivalent stress and total deformation.