Prediction of tensile strength distribution of CFRTP-SMC statistical modeling at various lengths

Carbon fiber reinforced thermoplastic sheet molding compounds (CFRTP-SMC) have demonstrated their utility across various industrial sectors, particularly in the automotive field. CFRTP material has several superiorities such as high-speed molding, good recyclability, and suitable for complex design. Despite their advantages, the complex internal structure of CFRTP-SMC complicates the accurate prediction of their mechanical properties, thus hindering their broader application. This study introduces a simulation methodology for evaluating the tensile strength of CFRTP-SMC via a Monte Carlo simulation-inspired approach. This novel strategy enables the prediction of variations in mechanical properties, which is difficult to achieve using existing techniques. We develop a simplified three-dimensional CFRTP-SMC simulation model that calculates the mean and variance of tensile strength implemented in Python using scientific computing libraries. The designation of algorithm for tape distribution and cross-layer is based on the materials available in laboratory. A significant challenge in using the simulation model is the necessity to consider various failure modes; therefore, we incorporate Hashin failure criterion to evaluate the tape failure mode, specifically focus on overlap area. The strength predictions of our model are consistent with published experimental data pertaining to CFRTP-SMC materials, thus demonstrating its efficacy. Consequently, the proposed model provides a solid foundation for predicting the mechanical properties of CFRTP-SMC with different tape geometries, thereby facilitating their broader application. • Developed a Monte-Carlo simulation method for tensile strength prediction of CFRTP-SMC material • Introduced a 3D model to calculate both mean and variance of mechanical properties • Incorporated Hashin failure criterion for accurate tape failure prediction • Simulation results align with experimental data, confirming the effectiveness of model