Optimising Mechanical Performance of Additive Manufactured Composites for Biomedical Applications

The mechanical properties of additive manufactured (AM) short-fibre reinforced polymer (SFRP) composites are significantly influenced by infill patterns, fibre orientation, and fibre-matrix interactions. While previous studies have explored the role of process parameters in optimising AM components, the impact of infill geometry on anisotropy and mechanical performance remains underexplored, particularly in the context of machine learning (ML). This study develops an ML-driven framework to predict the tensile and flexural properties of AM SFRP composites with different infill patterns, including triangular, hexagonal, and rectangular. AM structures were fabricated and subjected to tensile and flexural tests, with the data used to train ML models, including LightGBM, XGBoost, and artificial neural networks (ANN). The results showed that the triangular infill pattern had the highest tensile strength and stiffness, the hexagonal infill had the lowest flexural properties, and the rectangular infill exhibited performance levels that fell between those of the triangular and hexagonal patterns. The ML models demonstrated high prediction accuracy, with R-squared values exceeding 0.95. XGBoost performed best for predicting tensile properties of hexagonal infill, while ANN excelled with triangular and rectangular configurations. This study demonstrates the potential of machine learning to enhance the mechanical performance of additively manufactured SFRP composites by capturing the complex interplay between infill geometry and fibre-matrix interactions. Thus, providing additional data for the design of high-performance materials in applications such as biomedical devices.