Evaluation of flexural strength of 3D-Printed nylon with carbon reinforcement: An experimental validation using ANN

This study investigates the flexural strength of 3D-printed nylon-carbon reinforced composite specimens, highlighting the impact of infill density and layer height on mechanical performance. The findings indicate that a printing layer height of 0.10 mm with 100 % infill density exhibits the highest flexural strength, supporting a maximum load of 127 N, compared to 76.7 N at 50 % infill density. Microstructural study has clearly illustrated the structural distortion, revealing that a rise in layer height correlates with an escalation in structural distortion. An Artificial Neural Network (ANN) model is thus utilized to achieve high predictive accuracy in order to predict flexural behaviour. R-values above 0.98 are obtained across training, validation, and test datasets, indicating that ANN-based modelling may be able to facilitate quick optimization of 3D printing parameters for high-performance applications. These findings establish carbon-reinforced nylon as a formidable competitor for use in industries such as aerospace and automotive, where strength and durability are important.