Bridging experiment and simulation: Image processing-driven FEA of carbon fiber powder reinforced LDPE

Short fiber-reinforced polymers are widely used in engineering applications due to their improved mechanical performance and processability. However, accurately predicting the elastic behavior of such composites remains a challenge, especially when fibers are randomly distributed and lack defined orientation. To address this, this study investigates the mechanical properties of composite materials developed by incorporating carbon fiber powder (CFP) into a low-density polyethylene (LDPE) matrix at different reinforcement ratios (3 %, 6 %, and 9 %). The effects of CFP content on elastic modulus, tensile strength, and ductility were examined experimentally and through finite element analysis (FEA). Scanning electron microscopy (SEM) was used to analyze the microstructure, and image processing techniques were applied to quantify fiber orientation, which was then integrated into the FEA framework (Oriented FEA). Results indicate that CFP addition increases stiffness and tensile strength while reducing ductility, with 3 % CFP specimens showing the most favorable balance of mechanical properties. The proposed image-based orientation modeling approach provides improved simulation fidelity and offers a scalable methodology for evaluating the mechanical behavior of short fiber-reinforced composites.