Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Composites Communications

DOI: 10.1016/j.coco.2024.102216

Authors: Ruochen Xu, Yi Wan, Jun Takahashi

Institution: The University of Tokyo

Language: English

Topic: Mechanical Behavior of Composites

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,610