Optimization of 3D printing parameters for thermal conductivity of carbon fiber reinforced PLA components

This study investigates the optimization of printing parameters to improve the thermal conductivity of carbon fiber-reinforced polylactic acid (PLA-CF) components fabricated using fused deposition modeling (FDM). Four key process parameters - infill density, layer thickness, nozzle temperature, and print speed - were systematically evaluated using a Taguchi L16 orthogonal array design combined with analysis of variance (ANOVA). The findings demonstrate that infill density is the most influential factor affecting thermal conductivity, followed by layer thickness, while nozzle temperature and print speed have moderate contributions. Interaction analyses reveal that high infill levels combined with thin layers substantially enhance thermal conductivity, especially when paired with moderate nozzle temperatures and low printing speeds. Conversely, at low infill densities, increasing nozzle temperature alone cannot compensate for the negative effects of porosity on heat transfer, highlighting the importance of parameter synchronization. Additionally, a regression model was developed, demonstrating a strong correlation between process parameters and thermal conductivity, enabling accurate estimation under different combinations of settings. The outcomes of this study provide practical guidelines for optimizing printing conditions and designing thermally functional PLA-CF components, expanding the potential of FDM-based composites in advanced engineering applications where enhanced heat dissipation is required.